Occlusion aware planning

ABSTRACT

Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

BACKGROUND

Various methods, apparatuses, and systems are utilized by autonomousvehicles to guide such autonomous vehicles through environmentsincluding any number of obstacles. For instance, autonomous vehiclesutilize route planning methods, apparatuses, and systems to guideautonomous vehicles through congested areas that may include othervehicles, buildings, pedestrians, or other objects. In some examples,the vehicles, buildings, and/or objects in an environment can blockareas of the environment from being visible to sensors of the autonomousvehicle, which can present challenges in planning a route through suchareas.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 is an environment illustrating a perspective view of an exampleocclusion grid comprising a plurality of occlusion fields in anenvironment, and illustrating a vehicle including an occlusionmonitoring component to determine occluded and un-occluded regions ofthe occlusion grid resulting from obstacles, in accordance withembodiments of the disclosure.

FIG. 2 is a pictorial flow diagram of an example process for determiningan occupancy of occlusion field(s) in an occlusion grid based on raycasting LIDAR data, in accordance with embodiments of the disclosure.

FIG. 3 is a pictorial flow diagram of an example process for determiningan occupancy of occlusion field(s) in an occlusion grid based onprojecting occlusion field(s) into image data, in accordance withembodiments of the disclosure.

FIG. 4 depicts a block diagram of an example system for implementing thetechniques described herein.

FIG. 5 is a pictorial flow diagram of an example process for generatingan occlusion grid based on region characteristics, in accordance withembodiments of the disclosure.

FIG. 6 is a pictorial flow diagram of an example process for evaluatingan occlusion grid at an intersection and controlling a vehicle totraverse the intersection, in accordance with embodiments of thedisclosure.

FIG. 7 is a pictorial flow diagram of another example process forevaluating an occlusion grid at an intersection and controlling avehicle to traverse the intersection, in accordance with embodiments ofthe disclosure.

FIG. 8 is a pictorial flow diagram of an example process for determiningan occlusion region associated with an object and for generatingpredicted trajectories for dynamic object(s) in the occlusion region, inaccordance with embodiments of the disclosure.

FIG. 9 is a pictorial flow diagram of an example process for evaluatinga topographic occlusion grid and for controlling a vehicle to traverse atopographic obstacle, in accordance with embodiments of the disclosure.

FIG. 10 depicts an example process for determining an occupancy of anocclusion field based on ray casting LIDAR data and/or based onprojecting an occlusion field into image data, and controlling a vehicleto traverse an environment, in accordance with embodiments of thedisclosure.

FIG. 11 depicts an example process for determining an occlusion gridbased on map data being indicative of the occlusion grid or based onidentifying an obstacle in the environment, and controlling a vehicle totraverse the environment, in accordance with embodiments of thedisclosure.

DETAILED DESCRIPTION

This disclosure is directed to techniques for controlling a vehicle,such as an autonomous vehicle, based on occluded areas in anenvironment. For example, an occluded area can be represented by anocclusion grid, which may be stored in connection with map data or canbe dynamically generated based on obstacles in an environment. Anocclusion grid can include a plurality of occlusion fields, which canrepresent discrete areas of an environment, such as driveable surfaces.In some examples, an occlusion field can indicate an occlusion state(e.g., indicating whether the location is visible to one or more sensorsof an autonomous vehicle) and an occupancy state (e.g., indicatingwhether the location is occupied by an object such as a vehicle,pedestrian, animal, etc.). In at least some examples, the occupancystate may further comprise an “indeterminate” state, which is to saythat, based on the available data, it may be currently unknown whetherthe occlusion field is occupied or not. In some cases, the occlusionstate and/or occupancy state can be determined using LIDAR data and/orimage data captured by sensors on the autonomous vehicle. For example,the LIDAR data can be ray casted to determine whether the LIDAR rayshave traveled through the regions of space represented by the occlusionfields. In some instances, a confidence level that the occlusion fieldis occupied or is not occupied can be based at least in part on a firstnumber of expected LIDAR returns associated with the occlusion fieldand/or a second number of actual LIDAR returns associated with theocclusion field. In some instances, a confidence level that theocclusion field is occupied or not can be based at least in part on anumber of LIDAR observations in a column of LIDAR returns with respectto a height of an occlusion field. In some instances, the occupancy ofan occlusion field can be based on image data. For example, image datacan be captured in an environment, and the image data can be segmentedto generate segmented image data indicating driveable regions (e.g.,road surfaces that are not occupied by an object). Location datarepresenting the occlusion field can be projected into the segmentedimage data to determine whether the occlusion field is associated with adriveable surface. The occlusion states and occupancy states can bedetermined for all occlusion fields of an occlusion grid. In someexamples, when the occlusion fields are all visible and unoccupied, theocclusion grid can be considered clear, and the autonomous vehicle canbe controlled to traverse the environment.

In some examples, LIDAR data and image data can be used to determine theocclusion state and occupancy state of an occlusion field. For example,for occlusion fields that are relatively close to the autonomousvehicle, a resolution of the LIDAR sensor may be sufficient to determinethe occlusion state and occupancy state of an occlusion field with aconfidence level (also referred to as a confidence value) that meets orexceeds a threshold value. In some instances, if the confidence leveldoes not meet or exceed a threshold (e.g., for occlusion fields that arerelatively farther away from the autonomous vehicle or if a number ofLIDAR rays associated with an occlusion field is below a threshold), theimage data can be used to replace or supplement a determinationregarding the occlusion state and/or occupancy state using the LIDARdata.

The techniques discussed herein can be used in a number of scenarios. Ina first scenario, the occlusion based planning can be used when anautonomous vehicle navigates intersections where the autonomous vehicleis executing complex maneuvers, such as unprotected left turns, freeright turns, or negotiating complex intersections. As can be understood,in such an example, oncoming traffic may not stop at the intersection,which can present challenges in determining when an intersection is safeto traverse. In one example, the autonomous vehicle can approach anintersection and stop at a stop line (e.g., that has been indicated inmap data). An occlusion grid can be accessed from the map data, and theautonomous vehicle can capture sensor data to determine the states ofthe occlusion fields. In some instances, the autonomous vehicle candetermine that a portion of the occlusion grid is occluded (e.g., due toan obstacle such as a parked car) and/or lacking sufficient informationto proceed. In some examples, the autonomous vehicle may slowly traverseinto the intersection to change a location of its sensors in theenvironment. The autonomous vehicle can continue to capture sensor datato determine the occlusion state and occupancy state of some or allocclusion fields in the occlusion region. If other vehicles aretraversing the intersection, the occlusion fields will be determined tobe occupied, and the autonomous vehicle may continue to wait for anappropriate time to traverse the intersection. If there are no othervehicles traversing the intersection, and the sensors are able to makedeterminations of the occupancy states of the fields, the occlusionfields will be unoccupied, the occlusion grid will be clear, and theautonomous vehicle may traverse the intersection. As can be understood,the occlusion based planning can be used for traversing may differentintersections, and is not limited to any particular intersections ormaneuvers.

In a second scenario, an autonomous vehicle may capture sensor data ofan environment and can determine that an object in the environment is“blocking” the sensors from sensing a portion of the environment. Insome instances, an occlusion grid can be dynamically associated with theobject. In some examples, although the autonomous vehicle may knowlittle information about the occlusion grid at an initial time (e.g.,especially the occupancy of occlusion fields obscured by the obstacle),in some examples the autonomous vehicle can infer a context of theocclusion fields by monitoring a region where objects traverse prior toentering the occlusion grid. For example, as the autonomous vehiclemonitors an occluded region of an occlusion grid, the autonomous vehiclecan monitor for any objects (e.g., vehicles, pedestrians, etc.) enteringthe occlusion grid. After a period of time, if no objects have enteredthe occlusion grid, and if no objects have exited the occlusion grid,the autonomous vehicle may infer that the occlusion grid is unoccupied,and may execute a vehicle trajectory as appropriate.

In a third scenario, an autonomous vehicle can capture sensor data of anenvironment to identify an object in the environment. The autonomousvehicle can associate an occlusion grid with the object, as discussedherein. In some instances, the autonomous vehicle can also identify oneor more dynamic objects, such as a pedestrian, in the environment thattraversed into the occlusion region. The autonomous vehicle can monitora track of the dynamic object prior to the object traversing into theocclusion region, and can generate one or more predicted trajectoriesassociated with the object. In some instances, the one or more predictedtrajectories can be based on a measured track of the dynamic objectand/or based on reasonable paths (e.g., across a crosswalk or sidewalk,a continuation of the most recent trajectory, etc.) and/or destinationsin the environment. Thus, even when a dynamic object is not observed, byvirtue of the object being in an occluded region, the autonomous vehiclecan incorporate predicted trajectories of the dynamic objects intodeterminations regarding control of the autonomous vehicle. Suchpredicted trajectories may be later confirmed by subsequent observationsbased on the dynamic object remaining in the occluded region or based onthe dynamic object leaving the occluded region.

A fourth scenario is directed to occlusion regions caused by topographicobstacles, such as the crest of a hill. For example, as a vehicleapproaches the crest (e.g., top) of a hill, the vehicle may not be ableto see the ground surface on the other side of the hill crest. In someexamples, a topographic occlusion grid can be associated with map datarepresenting the hill region to facilitate traversing the hill. Forexample, the topographic occlusion grid (and other occlusion gridsdiscussed herein) can include occlusion fields that represent athree-dimensional region of space in the environment. As can beunderstood, a vertical portion of the occlusion field may be visible tothe vehicle approaching the crest of a hill without the vehicle beingable to see the ground portion of the occlusion field. The vehicle cancapture sensor data of the environment, such as LIDAR data, and can raycast the LIDAR data through the occlusion fields of the occlusion gridto determine an occupancy of at least a portion of the occlusion field.Accordingly, by determining that a portion of the occlusion field isclear (e.g., an area of the occlusion field between 1-2 meters above theground), the autonomous vehicle can determine the occupancy state of theocclusion field (e.g., occupied, unoccupied, or indeterminate). Thus,using the techniques discussed herein, the autonomous vehicle can “seeover” the crest of the hill to acquire information about an environmentwithout capturing sensor data of all regions of the environment.

In some instances, a size of an occlusion grid to be stored inconnection with map data can be based at least in part on a number ofregion characteristics. In the context of an intersection, the regioncharacteristics can include, but are not limited to, a distance acrossthe intersection that the autonomous vehicle must travel (e.g., in orderto be clear of oncoming traffic); a speed limit of vehicles in theintersection (e.g., of oncoming traffic); a safety factor associatedwith the speed limit (e.g., to effectively increase the speed of trafficto be expected); an acceleration level and/or average velocity of theautonomous vehicle traversing the intersection distance; and the like.Particular examples of such are discussed in detail below. Thus, theocclusion grid can be sized such that the autonomous vehicle can safelytraverse a region when the occlusion grid is clear of obstacles.

In some instances, and as noted above, an occlusion grid can include aplurality of occlusion fields. In some instances, the occlusion fieldscan represent a temporal logic symbol that can be used in a temporallogic formula to evaluate or validate a trajectory of an autonomousvehicle. For example, the autonomous vehicle may be prevented frominitiating a trajectory until an occupancy state and/or occlusion stateof the occlusion fields are “unoccupied” and “un-occluded,”respectively. In some instances, a planner system for the autonomousvehicle can use temporal logic, such as linear temporal logic or signaltemporal logic, to evaluate trajectories for the autonomous vehicle.

The techniques discussed herein can improve a functioning of a computingdevice in a number of ways. For example, in the context of evaluating anocclusion grid, an area of the grid can be sized to ensure safetraversal of regions by the autonomous vehicle without excessiveresources devoted to unnecessary determinations about the environment.In some instances, using multiple sensor modalities (e.g., LIDARsensors, image sensors, RADAR sensors, etc.) can improve an overallconfidence level associated with an occlusion state or occupancy stateof an occlusion field. Improved trajectory generation can improve safetyoutcomes and can improve a rider experience (e.g., by ensuring anintersection is clear before initiating a trajectory, by reducingoccurrences of emergency braking, swerving, and the like). These andother improvements to the functioning of the computer and/or to the userexperience are discussed herein.

The techniques described herein can be implemented in a number of ways.Example implementations are provided below with reference to thefollowing figures. Although discussed in the context of an autonomousvehicle, the methods, apparatuses, and systems described herein can beapplied to a variety of systems (e.g., a robotic platform), and is notlimited to autonomous vehicles. In another example, the techniques canbe utilized in an aviation or nautical context, or in any system usingmachine vision. Additionally, the techniques described herein can beused with real data (e.g., captured using sensor(s)), simulated data(e.g., generated by a simulator), or any combination of the two.

FIG. 1 is an environment 100 illustrating a perspective view of anexample occlusion grid comprising a plurality of occlusion fields in anenvironment. FIG. 1 also illustrates a vehicle including an occlusionmonitoring component to determine occluded and un-occluded regions ofthe occlusion grid resulting from obstacles, in accordance withembodiments of the disclosure.

As illustrated, the environment 100 can include a vehicle 102 thatincludes one or more sensor system(s) 104 capturing data representingthe environment 100.

For the purpose of illustration, the vehicle 102 can be an autonomousvehicle configured to operate according to a Level 5 classificationissued by the U.S. National Highway Traffic Safety Administration, whichdescribes a vehicle capable of performing all safety critical functionsfor the entire trip, with the driver (or occupant) not being expected tocontrol the vehicle at any time. In such an example, since the vehicle102 can be configured to control all functions from start to stop,including all parking functions, it can be unoccupied. This is merely anexample, and the systems and methods described herein can beincorporated into any ground-borne, airborne, or waterborne vehicle,including those ranging from vehicles that need to be manuallycontrolled by a driver at all times, to those that are partially orfully autonomously controlled. Additional details associated with thevehicle 102 are described below.

In at least one example, and as noted above, the vehicle 102 can beassociated with sensor system(s) 104 that can be disposed on the vehicle102. The sensor system(s) 104 can include light detection and ranging(LIDAR) sensors, radio detection and ranging (RADAR) sensors, ultrasonictransducers, sound navigation and ranging (SONAR) sensors, locationsensors (e.g., global positioning system (GPS), compass, etc.), inertialsensors (e.g., inertial measurement units, accelerometers,magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity,depth, time of flight, etc.), wheel encoders, microphones, environmentsensors (e.g., temperature sensors, humidity sensors, light sensors,pressure sensors, etc.), etc. The sensor system(s) 104 can generatesensor data, which can be utilized by vehicle computing device(s) 106associated with the vehicle 102.

In at least one example, the vehicle computing device(s) 106 can utilizesensor data in an occlusion monitoring component 108. For example, theocclusion monitoring component 108 can access occlusion data and/or mapdata to determine occlusion state(s) and occupancy state(s) of occlusionfields in an occlusion grid.

By way of example, and without limitation, the vehicle 102 in theenvironment 100 is approaching an intersection 110. In some examples,the vehicle 102 can access map data and can determine that an occlusiongrid 112 is associated with the intersection 110. In some examples, theintersection 110 can represent an intersection where the vehicle 102 isrequired to yield to one-way traffic entering the intersection 110.

The occlusion grid 112 can include a plurality of occlusion fieldsdividing a portion of the environment 100 into discrete regions. In someexamples, an occlusion field 114 can represent an occlusion state (e.g.,an indication of whether the occlusion field 114 is within a sensedregion 116 associated with one or more sensors of the vehicle 102). Ascan be understood, the environment 100 may include one or more obstacles118, 120, and 122 that cause a portion of the occlusion grid 112 to falloutside of the sensed region 116 of the vehicle 102. Accordingly, anun-occluded region 124 of the occlusion grid 112 can represent a regionof the occlusion grid 112 that can be “seen” by the vehicle 102, whichis to say, the vehicle 102 can capture sensor data representing theun-occluded region 124. Similarly, an occluded region 126 can representa region of the occlusion grid 112 that cannot be “seen” by the vehicle102. As illustrated in FIG. 1, the occlusion fields in the occludedregion 126 are shaded gray to represent the lack of sensor datacorresponding to that region, while the un-occluded region 124 is notshaded.

In some instances, when an occlusion field is occupied by an object, theocclusion field can store additional metadata indicating an identity ofthe object as well as data indicative of the path of the object throughthe occlusion grid.

Additional details directed to evaluating the occlusion fields usingLIDAR data and image data are discussed below in connection with FIGS. 2and 3, respectively.

FIG. 2 is a pictorial flow diagram of an example process 200 fordetermining an occupancy of occlusion field(s) in an occlusion gridbased on ray casting LIDAR data, in accordance with embodiments of thedisclosure. Although discussed in the context of LIDAR data, the exampleprocess 200 can be used in the context of RADAR data, SONAR data,time-of-flight image data, and the like.

At operation 202, the process can include receiving map data includingan occlusion grid. An example 204 illustrates map data 206 that can bereceived in the operation 202. In some instances, the map data 206 canrepresent the intersection 110 as represent in FIG. 1. As furtherillustrated in the example 204, the vehicle 102 captures LIDAR data ofthe environment, as illustrated by the LIDAR rays 208. Althoughillustrated as a single line, the LIDAR rays 208 can include a pluralityof light rays scanning the environment.

In some instances, the map data 206 includes an occlusion grid 210including a plurality of occlusion fields 216. As discussed throughoutthis disclosure, the occlusion grid 210 can represent any size or shapeof an environment. In some examples, the occlusion grid 210 can includea plurality of occlusion fields 216, whereby an individual occlusionfield represents a discrete region of the environment. In someinstances, an occlusion field may represent an area 1 meters wide, 3meters long, and 2 meters high, although any size may be used for theocclusion field. In some instances, an occlusion field may be consideredto be a “meta voxel” that represents a plurality of voxels of avoxelized space.

At operation 212, the process can include determining an occupancy ofone or more occlusion fields based at least in part on ray casting LIDARdata. In a detail 214 of the environment, an occlusion field 216 isillustrated with LIDAR rays traversing the occlusion field 216. Asindicated in the detail 214, LIDAR rays 218 and 220 are illustratedtraversing the occlusion field 216. As illustrated further in theexample 204, the LIDAR ray 218 can represent a LIDAR ray without areturn, meaning the LIDAR ray 218 was emitted by a LIDAR sensor andexpected to traverse through the occlusion field 216, but the vehicle102 did not receive a return of the LIDAR ray 218. Such a lack of areturn may be indeterminate of an occupancy state, as the LIDAR ray 218may not have hit a surface to return, or may have reflected off of areflective surface in a direction away from the LIDAR sensor. Further,the LIDAR ray 220 represents a LIDAR ray with a return, meaning theLIDAR ray 220 was emitted by a LIDAR sensor and was received by thevehicle 102 at a subsequent time (e.g., by virtue of reflecting off ofan object in the environment).

In some instances, the LIDAR rays 208 may indicate that an occlusionfield (e.g., the occlusion field 216) is occupied by an object, in whichcase the operation 212 can include determining that the occlusion field216 is occupied. In at least some examples, an occupancy may bedetermined based on a number of LIDAR returns in a vertical column abovea particular occlusion field 216 (e.g., as illustrated in a detail 226).As a non-limiting example, LIDAR returns may indicate that a region isoccupied or that a LIDAR ray traversed through a region before beingreceived as a reflection by a LIDAR sensor (e.g., the return mayindicate that the region is unoccupied). In some examples, a LIDAR raymay traverse through a region without being received by the LIDARsensor, discussed below.

As a non-limiting example, if a vertical column associated with theocclusion field 216 comprises 5 voxels (e.g., as illustrated by voxels216(1), 216(2), 216(3), 216(4), and 216(5) in the detail 226), and amajority of them indicate that an object is located in the voxels, theoccupancy state has met a threshold occupancy to determine the occlusionfield 216 is occupied. In other words, in some instances, an occupancyof an occlusion field 216 can be based at least in part on an occupancyscore associated with voxels associated with the occlusion field 216. Ofcourse, the occlusion field can be associated with any number ofvertical and/or horizontal voxels.

In some instances, the LIDAR rays 208 may indicate that the occlusionfield 216 is not occupied by an object. For example, if a majority ofthe voxels associated with the occlusion field 216 indicate no return(and/or based on the number of LIDAR rays traversing through a voxel andassociated with a return compared to a number of expected returns), itis most likely that there is no object occupying the occlusion field216, as opposed to all of the rays reflecting off a surface in adirection away from the LIDAR sensor. In some instances, a determinationregarding the occupancy of the occlusion field 216 can be associatedwith a confidence level. In some instances, a confidence level of anoccupancy of an occlusion field can be based at least in part on a firstnumber of expected LIDAR returns associated with the occlusion field anda second number of actual LIDAR returns (e.g., indicating an object orno object) associated with the occlusion field. By way of example, andwithout limitation, a confidence level can be based at least in part ona ratio of the second number and the first number. Of course, anytechniques or heuristics can be used to determine a confidence levelassociated with an occupancy of the occlusion field. In some examples,where a confidence level is not exceeded, where no sensor data isreturned, or where no determination of occupancy is otherwise able to bemade, the occupancy state associated with the occlusion field 216 may bedetermined to be indeterminate (e.g., there is not enough information todetermine the occupancy state).

At operation 222, the process can include commanding an autonomousvehicle based at least in part on the occupancy. For example, when thevehicle 102 determines that the occlusion field 216 is unoccupied (andthat all occlusion fields of the occlusion grid 210 or that a thresholdnumber of occlusion fields of the occlusion gird 210 are unoccupied),the vehicle 102 can be controlled to follow a trajectory 224 to traversethe intersection 110. In some examples, when the occlusion field 216 isoccupied and/or when threshold number of occlusion fields of theocclusion grid 210 are occupied by an object, the operation 222 caninclude controlling the autonomous vehicle (e.g., the vehicle 102) towait without traversing the intersection 110. In some examples, thethreshold may be set, for example, based on the maximum speed limitexpected, the lane width to traverse, and the like. Such a determinationmay be made, for example, based on the amount of time that it would takethe vehicle 102 to traverse the intersection safely, assuming a vehicleto be proceeding in the occluded area toward the intersection at themaximum speed limit (plus some buffer). Additional details are discussedin connection with FIG. 5.

In some examples, the operation 222 can include selecting anacceleration level and/or velocity for the vehicle 102. For example, anocclusion field may indicate an occupied occlusion field thateffectively reduces a size of the clear portion of the occlusion grid210. In some instances, the operation 222 can include selecting a higheracceleration level (e.g., a “boost mode”) causing the vehicle 102 toaccelerate faster based on the reduced size of the clear portion (e.g.,represented as the distance between the vehicle 102 and an occupiedocclusion field) of the occlusion grid 210. In some instances, theoperation 222 can include determining that a portion of the occlusiongrid is not visible (e.g., similar to the occluded region 126) orotherwise comprise occupancy states which are indeterminate, and theoperation 222 can include controlling the vehicle 102 to slowly moveinto the intersection (e.g., “creep forward”) to increase the visibleregion of the occlusion grid 210. Additional examples are discussedthroughout this disclosure.

FIG. 3 is a pictorial flow diagram of an example process 300 fordetermining an occupancy of occlusion field(s) in an occlusion gridbased on projecting occlusion field(s) into image data, in accordancewith embodiments of the disclosure.

At operation 302, the process can include receiving image data and mapdata. For example, image data 304 can be captured by one or more imagesensors of the vehicle 102. In some examples, map data 306 may beaccessed from memory (e.g., from the vehicle computing device(s) 106)upon determining that the vehicle 102 is approaching the intersection110 associated with the map data 306.

In general, the image data 304 can include any image data captured byone or more image sensors of a vehicle. In some cases, the image data304 can include representations of obstacles 308, 310, and 312. Asillustrated, the obstacles 308, 310, and 312 can represent vehiclesparked on the side of a road.

In general, the map data 306 can include an occlusion grid 314 includinga plurality of occlusion fields, such as an occlusion field 316. In someinstances, the map data 306 can include the obstacles 308, 310, and 312,although in some cases the data of the obstacles 308, 310, and 312 maynot be considered to be map data. That is, in some cases, the map data306 may only include static objects, in which case the obstacles 308,310, and 312 (e.g., dynamic objects or potentially dynamic objects) maynot be included as the map data 306. Further, the vehicle 102 may or maynot be considered part of the map data 306, although the vehicle 102 isshown in the map data 306 to provide context and to illustrate thelocation of the vehicle 102 with respect to the intersection 110.

As illustrated, the obstacle 312 is represented driving in the roadtowards the intersection 110. In some examples, the obstacle 312 canoccupy the occlusion field 326.

At operation 318, the process can include performing semanticsegmentation on the image data. In some examples, the image data 304 canbe input to a segmentation algorithm to identify regions and/or objectsin the image data 304, including but not limited to: driveable surfaces(e.g., road surfaces that are unoccupied by an object or obstacle),ground, vehicles, pedestrians, buildings, sidewalks, vegetation, lampposts, mailboxes, and the like. In some instances, the operation 318 caninclude segmenting the image data 304 using a machine learning algorithmtrained to segment and/or classify objects in the image data 304.

At operation 320, the process can include determining an occupancy ofone or more occlusion fields based at least in part on projecting theone or more occlusion fields into the segmented image data. For example,segmented image data with projected map data 322 illustrates theocclusion grid 314 and the occlusion fields 316 and 326 projected intothe image data 304. As further illustrated, the segmented image datawith projected map data 322 (also referred to as the data 322) includessegmented obstacles 308′, 310′, and 312′, which correspond to theobstacles 308, 310, and 312, respectively. Because the occlusion field326 projects onto the obstacle 312′, which can be determined to be avehicle (e.g., via the semantic segmentation), the operation 320 candetermine that at least the occlusion field 326 is occupied by theobstacle 312′.

The operation 320 can use any algorithm to project the three-dimensionalocclusion grid 314 and/or the occlusion field 316 into the image data304. In one example, if the occlusion field 316 projects into a regionlabeled as a driveable surface, the operation 320 can determine that theocclusion field 316 represents an unoccupied field. In some examples, asthe occlusion field 326 projects into the object 312′, the operation 320can determine that the occlusion field 326 is occupied. In at least someexamples, addition occlusion fields may correspond to an occlusion statewhich is indeterminate. In other words, because the image of an objecteffectively blocks some of the occlusion fields, it may be impossible tostate whether the occlusion field is occupied or not. Regardless, in anyof the examples described herein, an indeterminate state may be treatedas an occluded state for the purposes of safety.

At operation 324, the process can include commanding an autonomousvehicle (e.g., the vehicle 102) based at least in part on the occupancy.For example, when the vehicle 102 determines that the occlusion field316 is unoccupied (and that all occlusion fields of the occlusion grid314 are unoccupied), the vehicle 102 can be controlled to follow atrajectory to traverse the intersection 110. In some examples, when theocclusion field 326 is occupied, indeterminate, and/or when thresholdnumber of occlusion fields of the occlusion grid 314 are occupied by anobject, the operation 324 can include controlling the autonomous vehicle(e.g., the vehicle 102) to wait without traversing the intersection 110.In some examples, where a sufficiency of information is below athreshold value (e.g., a number of occupied or indeterminate occlusionfields meets or exceeds a threshold value and/or the number of occludedand non-visible occlusion fields meet or exceed some threshold such thata planner is unable to determine a safe trajectory to navigate thearea), the operation 324 can include commanding the vehicle 102 to creepforward or otherwise change a location of the vehicle 102 to gatheradditional information.

In some examples, a sufficiency of information can correspond todetermining a number of occlusion fields that are unoccupied meets orexceeds a threshold number and/or the number of visible occlusion fieldsmeets or exceeds a certain number (e.g. enough to surpass a certainthreshold for safely planning a trajectory to cross the region). In someinstances, a sufficiency of information can include observing anindeterminate occlusion field for a period of time to increase aconfidence level that the occlusion field is unoccupied. In someinstances, a sufficiency of information can include determining anextent of an un-occluded and unoccupied region relative to a distance ofan intersection for an autonomous vehicle to travel and/or relative to avelocity, acceleration, and/or time for the autonomous vehicle totraverse a portion of the intersection.

In some instances, the processes 200 and 300 can be performed inparallel (e.g., substantially simultaneously) and the outputs of eachprocess can be compared to increase an overall confidence level aboutthe states of one or more occlusion fields and/or about the states of anocclusion grid. In some instances, LIDAR data can be used to determinestates of occlusion fields that are within a threshold distance to thevehicle 102, while the image data can be used to determine states ofocclusion fields that meet or exceed a threshold distance from thevehicle 102. Of course, the examples described herein are not intendedto be limiting, and other implementations are considered within thescope of the disclosure.

FIG. 4 depicts a block diagram of an example system 400 for implementingthe techniques described herein. In at least one example, the system 400can include a vehicle 402, which can correspond to the vehicle 102 inFIG. 1.

The vehicle 402 can include a vehicle computing device 404, one or moresensor systems 406, one or more emitters 408, one or more communicationconnections 410, at least one direct connection 412, and one or moredrive modules 414.

The vehicle computing device 404 can include one or more processors 416and memory 418 communicatively coupled with the one or more processors416. In the illustrated example, the vehicle 402 is an autonomousvehicle; however, the vehicle 402 could be any other type of vehicle, orany other system having at least an image capture device (e.g., a cameraenabled smartphone). In the illustrated example, the memory 418 of thevehicle computing device 404 stores a localization component 420, aperception component 422, a planning component 424, one or more systemcontrollers 426, one or more maps 428 a prediction component 430, anocclusion monitoring component 432, an occlusion region component 434, aray casting component 436, a segmentation component 438, a projectioncomponent 440, an occlusion context component 442, and a velocitycomponent 444. Though depicted in FIG. 4 as residing in the memory 418for illustrative purposes, it is contemplated that the localizationcomponent 420, the perception component 422, the planning component 424,the one or more system controllers 426, the one or more maps 428, theprediction component 430, the occlusion monitoring component 432, theocclusion region component 434, the ray casting component 436, thesegmentation component 438, the projection component 440, the occlusioncontext component 442, and the velocity component 444 can additionally,or alternatively, be accessible to the vehicle 402 (e.g., stored on, orotherwise accessible by, memory remote from the vehicle 402). In someinstances, the vehicle computing device(s) 404 can correspond to thevehicle computing device(s) 106 of FIG. 1.

In at least one example, the localization component 420 can includefunctionality to receive data from the sensor system(s) 406 to determinea position and/or orientation of the vehicle 402 (e.g., one or more ofan x-, y-, z-position, roll, pitch, or yaw). For example, thelocalization component 420 can include and/or request/receive a map ofan environment and can continuously determine a location and/ororientation of the autonomous vehicle within the map. In some instances,the localization component 420 can utilize SLAM (simultaneouslocalization and mapping), CLAMS (calibration, localization and mapping,simultaneously), relative SLAM, bundle adjustment, non-linear leastsquares optimization, or the like to receive image data, LIDAR data,radar data, IMU data, GPS data, wheel encoder data, and the like toaccurately determine a location of the autonomous vehicle. In someinstances, the localization component 420 can provide data to variouscomponents of the vehicle 402 to determine an initial position of anautonomous vehicle for generating a trajectory and/or for determining toretrieve map data including an occlusion grid from memory, as discussedherein.

In some instances, the perception component 422 can includefunctionality to perform object detection, segmentation, and/orclassification. In some examples, the perception component 422 canprovide processed sensor data that indicates a presence of an entitythat is proximate to the vehicle 402 and/or a classification of theentity as an entity type (e.g., car, pedestrian, cyclist, animal,building, tree, road surface, curb, sidewalk, unknown, etc.). Inadditional and/or alternative examples, the perception component 422 canprovide processed sensor data that indicates one or more characteristicsassociated with a detected entity (e.g., a tracked object) and/or theenvironment in which the entity is positioned. In some examples,characteristics associated with an entity can include, but are notlimited to, an x-position (global and/or local position), a y-position(global and/or local position), a z-position (global and/or localposition), an orientation (e.g., a roll, pitch, yaw), an entity type(e.g., a classification), a velocity of the entity, an acceleration ofthe entity, an extent of the entity (size), etc. Characteristicsassociated with the environment can include, but are not limited to, apresence of another entity in the environment, a state of another entityin the environment, a time of day, a day of a week, a season, a weathercondition, an indication of darkness/light, etc. In some instances, theperception component 422 can operate in conjunction with thesegmentation component 438 to segment image data, as discussed herein.

In general, the planning component 424 can determine a path for thevehicle 402 to follow to traverse through an environment. For example,the planning component 424 can determine various routes and trajectoriesand various levels of detail. For example, the planning component 424can determine a route to travel from a first location (e.g., a currentlocation) to a second location (e.g., a target location). For thepurpose of this discussion, a route can be a sequence of waypoints fortravelling between two locations. As non-limiting examples, waypointsinclude streets, intersections, global positioning system (GPS)coordinates, etc. Further, the planning component 424 can generate aninstruction for guiding the autonomous vehicle along at least a portionof the route from the first location to the second location. In at leastone example, the planning component 424 can determine how to guide theautonomous vehicle from a first waypoint in the sequence of waypoints toa second waypoint in the sequence of waypoints. In some examples, theinstruction can be a trajectory, or a portion of a trajectory. In someexamples, multiple trajectories can be substantially simultaneouslygenerated (e.g., within technical tolerances) in accordance with areceding horizon technique, wherein one of the multiple trajectories isselected for the vehicle 402 to navigate.

In some instances, the planning component 424 can generate one or moretrajectories for the vehicle 402 based at least in part on states ofocclusion field(s) and/or occlusion grids, as discussed herein. In someexamples, the planning component 424 can use temporal logic, such aslinear temporal logic and/or signal temporal logic, to evaluate one ormore trajectories of the vehicle 402. Details of utilizing temporallogic in the planning component 424 are discussed in U.S. applicationSer. No. 15/632,147, which is herein incorporated by reference, in itsentirety.

In at least one example, the vehicle computing device 404 can includeone or more system controllers 426, which can be configured to controlsteering, propulsion, braking, safety, emitters, communication, andother systems of the vehicle 402. These system controller(s) 426 cancommunicate with and/or control corresponding systems of the drivemodule(s) 414 and/or other components of the vehicle 402.

The memory 418 can further include one or more maps 428 that can be usedby the vehicle 402 to navigate within the environment. For the purposeof this discussion, a map can be any number of data structures modeledin two dimensions, three dimensions, or N-dimensions that are capable ofproviding information about an environment, such as, but not limited to,topologies (such as intersections), streets, mountain ranges, roads,terrain, and the environment in general. In some instances, a map caninclude, but is not limited to: texture information (e.g., colorinformation (e.g., RGB color information, Lab color information, HSV/HSLcolor information), and the like), intensity information (e.g., LIDARinformation, RADAR information, and the like); spatial information(e.g., image data projected onto a mesh, individual “surfels” (e.g.,polygons associated with individual color and/or intensity)),reflectivity information (e.g., specularity information,retroreflectivity information, BRDF information, BSSRDF information, andthe like). In one example, a map can include a three-dimensional mesh ofthe environment. In some instances, the map can be stored in a tiledformat, such that individual tiles of the map represent a discreteportion of an environment, and can be loaded into working memory asneeded. In at least one example, the one or more maps 428 can include atleast one map (e.g., images and/or a mesh). In some examples, thevehicle 402 can be controlled based at least in part on the maps 428.That is, the maps 428 can be used in connection with the localizationcomponent 420, the perception component 422, and/or the planningcomponent 424 to determine a location of the vehicle 402, identifyobjects in an environment, and/or generate routes and/or trajectories tonavigate within an environment.

In some examples, the one or more maps 428 can be stored on a remotecomputing device(s) (such as the computing device(s) 448) accessible vianetwork(s) 446. In some examples, multiple maps 428 can be stored basedon, for example, a characteristic (e.g., type of entity, time of day,day of week, season of the year, etc.). Storing multiple maps 428 canhave similar memory requirements, but increase the speed at which datain a map can be accessed.

In some examples, the one or more maps 428 can store occlusion gridsassociated with individual locations in an environment. For example, asthe vehicle 402 traverses the environment and as maps representing anarea proximate to the vehicle 402 are loaded into memory, one or moreocclusion grids associated with a location can be loaded into memory aswell.

In some examples, the prediction component 430 can include functionalityto generate predicted trajectories of objects in an environment. Forexample, the prediction component 430 can generate one or more predictedtrajectories for vehicles, pedestrians, animals, and the like within athreshold distance from the vehicle 402. In some instances, and asdiscussed in connection with FIG. 8, the prediction component 430 canmeasure a trace of an object and generate a trajectory for the objectwhen the object enters an occlusion region in an environment. Thus, evenin a case where an object is not visible to one or more sensors of thevehicle 402, the prediction component 430 can extend a trajectory of theobject in memory so as to maintain data about the object even if theobject is not captured in sensor data.

In general, the occlusion monitoring component 432 can includefunctionality to generate and/or access occlusion grid data, determineoccluded and un-occluded regions of an occlusion grid, and to determineocclusion states and/or occupancy states for occlusion fields of anocclusion grid. In some instances, the occlusion monitoring component432 can correspond to the occlusion monitoring component 108 of FIG. 1.As discussed herein, the occlusion monitoring component 432 can receiveLIDAR data, image data, map data, and the like to determineocclusion-related information in an environment. In some instances, theocclusion monitoring component 432 can provide occlusion information tothe planning component 424 to determine when to control the vehicle 402to traverse an environment. occlusion monitoring component 432 canprovide occlusion information to the prediction component 430 togenerate predicted trajectories for one or more objects in an occlusionregion in an environment. Additional details of the occlusion monitoringcomponent 432 are discussed in connection with the remaining componentsstored in the memory 418.

The occlusion region component 434 can include functionality todetermine a portion of an occlusion grid that is occluded (e.g., similarto the occluded region 126 of FIG. 1) and to determine a portion of theocclusion grid that is un-occluded (e.g., similar to the un-occludedregion 124 of FIG. 1). For example, the occlusion region component 434can receive LIDAR data, image data, RADAR data, and the like todetermine whether a region is “visible” to one or more sensors of thevehicle 402. In some instances, the occlusion region component 434 caninclude functionality to project sensor data into map data to determineregions of the map where no data is located. In some instances, the mapdata can be used to determine that there are regions “outside” theobservable regions to determine regions that are occluded regions. Insome instances, the occlusion region component 434 can dynamicallygenerate an occluded region based on objects in an environment.

The ray casting component 436 can include functionality to receive LIDARdata and to ray cast the LIDAR data through the occlusion fields todetermine an occlusion state and/or an occupancy state of a particularocclusion field. In some examples, the ray casting component 436 candetermine an expected number of LIDAR returns associated with aparticular occlusion field and can determine a number of actual LIDARreturns associated with a particular field. In some instances, the raycasting component 436 can determine a confidence level associated withthe occlusion state and/or the occupancy state of an occlusion field, asdiscussed herein. As can be understood, the functionality discussedherein is not limited to LIDAR data, and can be used to determine anocclusion state and/or occupancy state using multiple sensortechnologies (e.g., RADAR, SONAR, time-of-flight imagers, depth cameras,etc.).

The segmentation component 438 can include functionality to receiveimage data and to segment the image data to identity various objectsand/or regions represented in the image data. For example, thesegmentation component 438 can include one or more machine learningalgorithms trained to identify and segment image data into semanticcategories, including but not limited to, a driveable surface, freespace (e.g., driveable surfaces) and/or non-free space, a vehicle, apedestrian, a building, vegetation, and the like. In some instances, thesegmentation component 438 can operate in conjunction with theperception component 422 to perform semantic segmentation on image dataand/or on LIDAR data, for example.

The projection component 440 can include functionality to receivesegmented image data and map data including an occlusion grid andocclusion field(s) and to project the occlusion field(s) into segmentedimage data. In some instances, the projection component 440 candetermine an occupancy of one or more occlusion fields by determiningwhether the occlusion field projects into a driveable surface (e.g., aclear road), into an object (e.g., a vehicle) that would occupy theregion, or otherwise indeterminate.

The occlusion context component 442 can include functionality todetermine an occupancy of an occlusion grid, and especially an occludedregion, based on the context of the environment. As can be understood, aportion of an occlusion grid can be occluded by an object such that thevehicle 402 may not capture sensor data of an occluded region. In someexamples, the occlusion context component 442 can monitor a region thatis physically before the occluded region (e.g., in terms of a directionof traffic flow) to determine whether objects enter the occluded region.Based on a size of the occluded region, and after a passage of time, theocclusion context component 442 can determine that an occluded region isnot occupied if no objects have entered the occluded region. As anon-limiting example, such a context component 442 can determine thepresence of a vehicle approaching an intersection of a two-lane roadand, based on determining that no objects were detecting passing behindthe occluded region, the context component 442 can determine that theoccluded regions are unoccupied. In some instances, a confidence levelassociated with the determination of an occupancy state can increaseover time (e.g., by accumulating additional observations). In someinstances, for a dynamically generated occlusion grid, a lifetime of theocclusion grid can correspond to an amount of time since the occlusiongrid was instantiated. In some instances, a confidence level of anoccupancy state of an occlusion grid can be based at least in part onthe lifetime of the occlusion grid. In some instances, the occlusioncontext component 442 can receive LIDAR data, image data, RADAR data,and the like to determine a context of an occluded region.

The velocity component 444 can include functionality to determine avelocity and/or an acceleration level for the vehicle 402 whiletraversing an environment. In some instances, the velocity and/oracceleration levels can include, but are not limited to, a low velocity(e.g., a “creep mode”), a normal velocity (e.g., a nominal velocity),and a high velocity (e.g., a “boost mode”). For example, a low velocitylevel can be selected for the vehicle 402 to enter an intersection or aregion without traversing the entire intersection or region. Forexample, and as discussed in connection with FIG. 6, the vehicle 402 maybe controlled to move slowly along a trajectory to “see” more of aregion to reduce a size of an occluded region before progressingaccording to nominal controls. For such a trajectory, the velocitycomponent 444 can select a low velocity. As another example, when anocclusion grid is clear, the normal velocity can be selected for thevehicle 402 to traverse an environment. In some cases, where a size ofan occlusion grid is reduced due to obstacles and/or based on limitedsightlines in the environment, a high velocity can be selected to reducean amount of time the vehicle 402 can be exposed to oncoming traffic. Insome instances, the velocity component 444 can operate in conjunctionwith the planning component 424 and/or the system controller(s) 426 tocontrol the acceleration of the vehicle 402.

As can be understood, the components discussed herein (e.g., thelocalization component 420, the perception component 422, the planningcomponent 424, the one or more system controllers 426, the one or moremaps 428, the prediction component 430, the occlusion monitoringcomponent 432, the occlusion region component 434, the ray castingcomponent 436, the segmentation component 438, the projection component440, the occlusion context component 442, and the velocity component444) are described as divided for illustrative purposes. However, theoperations performed by the various components can be combined orperformed in any other component. By way of example, segmentationfunctions may be performed by the perception component 422 (e.g., ratherthan the segmentation component 438) to reduce the amount of datatransferred by the system.

In some instances, aspects of some or all of the components discussedherein can include any models, algorithms, and/or machine learningalgorithms. For example, in some instances, the components in the memory418 (and the memory 452, discussed below) can be implemented as a neuralnetwork.

As described herein, an exemplary neural network is a biologicallyinspired algorithm which passes input data through a series of connectedlayers to produce an output. Each layer in a neural network can alsocomprise another neural network, or can comprise any number of layers(whether convolutional or not). As can be understood in the context ofthis disclosure, a neural network can utilize machine learning, whichcan refer to a broad class of such algorithms in which an output isgenerated based on learned parameters.

Although discussed in the context of neural networks, any type ofmachine learning can be used consistent with this disclosure. Forexample, machine learning algorithms can include, but are not limitedto, regression algorithms (e.g., ordinary least squares regression(OLSR), linear regression, logistic regression, stepwise regression,multivariate adaptive regression splines (MARS), locally estimatedscatterplot smoothing (LOESS)), instance-based algorithms (e.g., ridgeregression, least absolute shrinkage and selection operator (LASSO),elastic net, least-angle regression (LARS)), decisions tree algorithms(e.g., classification and regression tree (CART), iterative dichotomiser3 (ID3), Chi-squared automatic interaction detection (CHAID), decisionstump, conditional decision trees), Bayesian algorithms (e.g., naïveBayes, Gaussian naïve Bayes, multinomial naïve Bayes, averageone-dependence estimators (AODE), Bayesian belief network (BNN),Bayesian networks), clustering algorithms (e.g., k-means, k-medians,expectation maximization (EM), hierarchical clustering), associationrule learning algorithms (e.g., perceptron, back-propagation, hopfieldnetwork, Radial Basis Function Network (RBFN)), deep learning algorithms(e.g., Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN),Convolutional Neural Network (CNN), Stacked Auto-Encoders),Dimensionality Reduction Algorithms (e.g., Principal Component Analysis(PCA), Principal Component Regression (PCR), Partial Least SquaresRegression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS),Projection Pursuit, Linear Discriminant Analysis (LDA), MixtureDiscriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA),Flexible Discriminant Analysis (FDA)), Ensemble Algorithms (e.g.,Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, StackedGeneralization (blending), Gradient Boosting Machines (GBM), GradientBoosted Regression Trees (GBRT), Random Forest), SVM (support vectormachine), supervised learning, unsupervised learning, semi-supervisedlearning, etc.

Additional examples of architectures include neural networks such asResNet70, ResNet101, VGG, DenseNet, PointNet, and the like.

In at least one example, the sensor system(s) 406 can include LIDARsensors, radar sensors, ultrasonic transducers, sonar sensors, locationsensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertialmeasurement units (IMUs), accelerometers, magnetometers, gyroscopes,etc.), cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.),microphones, wheel encoders, environment sensors (e.g., temperaturesensors, humidity sensors, light sensors, pressure sensors, etc.), etc.The sensor system(s) 406 can include multiple instances of each of theseor other types of sensors. For instance, the LIDAR sensors can includeindividual LIDAR sensors located at the corners, front, back, sides,and/or top of the vehicle 402. As another example, the camera sensorscan include multiple cameras disposed at various locations about theexterior and/or interior of the vehicle 402. The sensor system(s) 406can provide input to the vehicle computing device 404. Additionally oralternatively, the sensor system(s) 406 can send sensor data, via theone or more networks 446, to the one or more computing device(s) at aparticular frequency, after a lapse of a predetermined period of time,in near real-time, etc. In some instances, the sensor system(s) 406 cancorrespond to the sensor system(s) 104 of FIG. 1.

The vehicle 402 can also include one or more emitters 408 for emittinglight and/or sound, as described above. The emitters 408 in this exampleinclude interior audio and visual emitters to communicate withpassengers of the vehicle 402. By way of example and not limitation,interior emitters can include speakers, lights, signs, display screens,touch screens, haptic emitters (e.g., vibration and/or force feedback),mechanical actuators (e.g., seatbelt tensioners, seat positioners,headrest positioners, etc.), and the like. The emitters 408 in thisexample also include exterior emitters. By way of example and notlimitation, the exterior emitters in this example include lights tosignal a direction of travel or other indicator of vehicle action (e.g.,indicator lights, signs, light arrays, etc.), and one or more audioemitters (e.g., speakers, speaker arrays, horns, etc.) to audiblycommunicate with pedestrians or other nearby vehicles, one or more ofwhich comprising acoustic beam steering technology.

The vehicle 402 can also include one or more communication connection(s)410 that enable communication between the vehicle 402 and one or moreother local or remote computing device(s). For instance, thecommunication connection(s) 410 can facilitate communication with otherlocal computing device(s) on the vehicle 402 and/or the drive module(s)414. Also, the communication connection(s) 410 can allow the vehicle tocommunicate with other nearby computing device(s) (e.g., other nearbyvehicles, traffic signals, etc.). The communications connection(s) 410also enable the vehicle 402 to communicate with a remote teleoperationscomputing device or other remote services.

The communications connection(s) 410 can include physical and/or logicalinterfaces for connecting the vehicle computing device 404 to anothercomputing device or a network, such as network(s) 446. For example, thecommunications connection(s) 410 can enable Wi-Fi-based communicationsuch as via frequencies defined by the IEEE 802.11 standards, shortrange wireless frequencies such as Bluetooth®, cellular communication(e.g., 2G, 4G, 4G, 4G LTE, 5G, etc.) or any suitable wired or wirelesscommunications protocol that enables the respective computing device tointerface with the other computing device(s).

In at least one example, the vehicle 402 can include one or more drivemodules 414. In some examples, the vehicle 402 can have a single drivemodule 414. In at least one example, if the vehicle 402 has multipledrive modules 414, individual drive modules 414 can be positioned onopposite ends of the vehicle 402 (e.g., the front and the rear, etc.).In at least one example, the drive module(s) 414 can include one or moresensor systems to detect conditions of the drive module(s) 414 and/orthe surroundings of the vehicle 402. By way of example and notlimitation, the sensor system(s) can include one or more wheel encoders(e.g., rotary encoders) to sense rotation of the wheels of the drivemodules, inertial sensors (e.g., inertial measurement units,accelerometers, gyroscopes, magnetometers, etc.) to measure orientationand acceleration of the drive module, cameras or other image sensors,ultrasonic sensors to acoustically detect objects in the surroundings ofthe drive module, LIDAR sensors, radar sensors, etc. Some sensors, suchas the wheel encoders can be unique to the drive module(s) 414. In somecases, the sensor system(s) on the drive module(s) 414 can overlap orsupplement corresponding systems of the vehicle 402 (e.g., sensorsystem(s) 406).

The drive module(s) 414 can include many of the vehicle systems,including a high voltage battery, a motor to propel the vehicle, aninverter to convert direct current from the battery into alternatingcurrent for use by other vehicle systems, a steering system including asteering motor and steering rack (which can be electric), a brakingsystem including hydraulic or electric actuators, a suspension systemincluding hydraulic and/or pneumatic components, a stability controlsystem for distributing brake forces to mitigate loss of traction andmaintain control, an HVAC system, lighting (e.g., lighting such ashead/tail lights to illuminate an exterior surrounding of the vehicle),and one or more other systems (e.g., cooling system, safety systems,onboard charging system, other electrical components such as a DC/DCconverter, a high voltage j unction, a high voltage cable, chargingsystem, charge port, etc.). Additionally, the drive module(s) 414 caninclude a drive module controller which can receive and preprocess datafrom the sensor system(s) and to control operation of the variousvehicle systems. In some examples, the drive module controller caninclude one or more processors and memory communicatively coupled withthe one or more processors. The memory can store one or more modules toperform various functionalities of the drive module(s) 414. Furthermore,the drive module(s) 414 also include one or more communicationconnection(s) that enable communication by the respective drive modulewith one or more other local or remote computing device(s).

In at least one example, the direct connection 412 can provide aphysical interface to couple the one or more drive module(s) 414 withthe body of the vehicle 402. For example, the direction connection 412can allow the transfer of energy, fluids, air, data, etc. between thedrive module(s) 414 and the vehicle. In some instances, the directconnection 412 can further releasably secure the drive module(s) 414 tothe body of the vehicle 402.

In at least one example, the localization component 420, the perceptioncomponent 422, the planning component 424, the one or more systemcontrollers 426, the one or more maps 428, the prediction component 430,the occlusion monitoring component 432, the occlusion region component434, the ray casting component 436, the segmentation component 438, theprojection component 440, the occlusion context component 442, and thevelocity component 444 can process sensor data, as described above, andcan send their respective outputs, over the one or more network(s) 446,to one or more computing device(s) 448. In at least one example, thelocalization component 420, the perception component 422, the planningcomponent 424, the one or more system controllers 426, the one or moremaps 428, the prediction component 430, the occlusion monitoringcomponent 432, the occlusion region component 434, the ray castingcomponent 436, the segmentation component 438, the projection component440, the occlusion context component 442, and the velocity component 444can send their respective outputs to the one or more computing device(s)448 at a particular frequency, after a lapse of a predetermined periodof time, in near real-time, etc.

In some examples, the vehicle 402 can send sensor data to one or morecomputing device(s) 448 via the network(s) 446. In some examples, thevehicle 402 can send raw sensor data to the computing device(s) 448. Inother examples, the vehicle 402 can send processed sensor data and/orrepresentations of sensor data to the computing device(s) 448. In someexamples, the vehicle 402 can send sensor data to the computingdevice(s) 448 at a particular frequency, after a lapse of apredetermined period of time, in near real-time, etc. In some cases, thevehicle 402 can send sensor data (raw or processed) to the computingdevice(s) 448 as one or more log files.

The computing device(s) 448 can include processor(s) 450 and a memory452 storing a maps(s) component 454.

In some instances, the map(s) component 454 can include functionality toassociate occlusion grid(s) with map locations. In some instances, themap(s) component 454 can identify intersections where a vehicleapproaching the intersection does not have a right of way, for example,and can generate an occlusion grid for that location. In some instances,the map(s) component 454 can identify topographic occlusion regionscaused by a crest of a hill, for example, that may reduce a visibilityof the vehicle 402. In some instances, and as discussed in connectionwith FIG. 5, for example, a size of the occlusion grid can be based atleast in part on region characteristics such as a distance of anintersection, a speed limit, a speed limit safety factor, and the like.

The processor(s) 416 of the vehicle 402 and the processor(s) 450 of thecomputing device(s) 448 can be any suitable processor capable ofexecuting instructions to process data and perform operations asdescribed herein. By way of example and not limitation, the processor(s)416 and 450 can comprise one or more Central Processing Units (CPUs),Graphics Processing Units (GPUs), or any other device or portion of adevice that processes electronic data to transform that electronic datainto other electronic data that can be stored in registers and/ormemory. In some examples, integrated circuits (e.g., ASICs, etc.), gatearrays (e.g., FPGAs, etc.), and other hardware devices can also beconsidered processors in so far as they are configured to implementencoded instructions.

Memory 418 and 452 are examples of non-transitory computer-readablemedia. The memory 418 and 452 can store an operating system and one ormore software applications, instructions, programs, and/or data toimplement the methods described herein and the functions attributed tothe various systems. In various implementations, the memory can beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory capable ofstoring information. The architectures, systems, and individual elementsdescribed herein can include many other logical, programmatic, andphysical components, of which those shown in the accompanying figuresare merely examples that are related to the discussion herein.

It should be noted that while FIG. 4 is illustrated as a distributedsystem, in alternative examples, components of the vehicle 402 can beassociated with the computing device(s) 448 and/or components of thecomputing device(s) 448 can be associated with the vehicle 402. That is,the vehicle 402 can perform one or more of the functions associated withthe computing device(s) 448, and vice versa. Further, aspects of theocclusion monitoring component 432 and/or the prediction component 430can be performed on any of the devices discussed herein.

FIGS. 2, 3, and 5-11 illustrate example processes in accordance withembodiments of the disclosure. These processes are illustrated aslogical flow graphs, each operation of which represents a sequence ofoperations that can be implemented in hardware, software, or acombination thereof. In the context of software, the operationsrepresent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the processes.

FIG. 5 is a pictorial flow diagram of an example process 500 forgenerating an occlusion grid based on region characteristics, inaccordance with embodiments of the disclosure. For example, some or allof the process 500 can be performed by one or more components in FIG. 4,as described herein.

At operation 502, the process can include evaluating regioncharacteristics. In some examples, the region characteristics caninclude, but are not limited to, an intersection (or region) distance(e.g., a width of an intersection to be crossed), a speed limit, avehicle velocity, a safety factor. and the like. An example 504illustrates region characteristics for the intersection 110. Forexample, the intersection 110 is associated with an intersectiondistance 506, which represents a distance between a stop line 508 of theintersection 110 and a location associated with exiting the intersection110. In some examples, the vehicle 102 may not remain static in theintersection 110 when attempting to cross the intersection, andaccordingly, the vehicle 102 may be controlled to navigate to a checkline 510. In some examples, the check line 510 can represent thefurthest into the intersection 110 that the vehicle 102 can navigatewithout interrupting the flow of traffic. That is, beyond the check line510, the vehicle 102 may be in the way of oncoming traffic. In a casewhere the vehicle 102 moves to the check line 510, the intersectiondistance 506 can be updated to reflect the reduced distance of theintersection 110 to be traversed by the vehicle 102. In some instances,the stop line 508 and/or the check line 510 can be stored as map data orgenerated dynamically based on sensor data captured in an environment.

The intersection 110 may also be associated with a speed limit 512. Ascan be understood, a safety factor can be associated with the speedlimit 512 that effectively raises the speed vehicles traversing theintersection 110 are expected to reach. For example, in an intersectionwhere the speed limit 512 is 25 miles per hour, the safety factor may bean additional 15 miles per hour. That is to say, at least some vehicleswhich traverse the intersection 110 are presumed to be speeding. Such asafety factor accounts for this assumption.

The example 504 also illustrates the vehicle 102 associated with anaverage velocity, a “creep mode,” and a “boost mode.” As can beunderstood, the average velocity can be an expected average velocity forthe vehicle 102 to traverse the intersection distance 506. Additionally,the vehicle 102 can be associated with various acceleration levels toslowly or quickly traverse at least a portion of the intersection 110.

At operation 514, the process can include generating an occlusion gridbased at least in part on the region characteristics. In the example504, an occlusion grid is generated and associated with the intersection110. In some examples, the occlusion grid 516 can be associated with anocclusion grid length 518 (or more generally, an extent (e.g., length,width, height) of the occlusion grid 516). By way of example, andwithout limitation, the occlusion grid length 518 can be sized such thatthe vehicle 102 traversing the intersection 110 associated with theintersection distance 506 at an average velocity of the vehicle 102cannot be intersected by a vehicle traveling at the speed limit 512(plus a safety factor) if the occlusion grid 516 is clear when thevehicle 102 initiates its trajectory. That is, the occlusion grid length518 is sized to allow the vehicle 102 to safely traverse theintersection 110 when the occlusion grid 516 is clear. As can beunderstood, generating the occlusion grid 516 based at least in part onthe region characteristics improves safety outcomes without allocatingadditional resources to the operations than are required to ensure safetravel.

In some instances, a length of a visible region associated with anintersection may be restricted or limited due to the geography of theintersection. For example, a road prior to an intersection may include ahairpin turn or other obstruction that reduces a size of a region thatcan be sensed by the vehicle 102. In some instances, geographic and/ortopographic information about the intersection (e.g., the maximumvisible distance) can be used to set a size of the occlusion grid 516 toavoid a scenario where the vehicle 102 expects a clear region beyondwhat is physically possible. Accordingly, a velocity and/or accelerationof the vehicle can be set to ensure that the vehicle can safely traversesuch an intersection. Similarly, a threshold associated with a size ofan un-occluded region or a threshold associated with indeterminateoccupancy states can be adjusted based on a size of the region to besensed, as discussed herein.

FIG. 6 is a pictorial flow diagram of an example process 600 forevaluating an occlusion grid at an intersection and controlling avehicle to traverse the intersection, in accordance with embodiments ofthe disclosure. For example, some or all of the process 600 can beperformed by one or more components in FIG. 4, as described herein.

At operation 602, the process can include evaluating an occlusion gridat an intersection. In an example 604, the vehicle 102 is illustrated inthe intersection 110 at a stop line 606. Based at least in part on mapdata associated with the intersection 110, the vehicle 102 can load intomemory an occlusion grid 608. Additionally, the vehicle 102 candetermine the stop line 606 based on map data or can generate the stopline 606 based on identifying the stop line 606 in the environment.Additionally, a check line 610 can be accessed from memory or determinedbased on identifying a shoulder or protected region of the intersection110.

In some instances, due to the location of the vehicle 102 with respectto one or more obstacles 612 (here a row of parked cars) and a building632, the vehicle 102 can associate an un-occluded region 614 with aportion of the occlusion grid 608 and can associate an occluded region616 with a portion of the occlusion grid 608. As illustrated, individualocclusion fields of the occlusion grid corresponding to the occludedregion 616 are shaded as gray, while the un-occluded region 614 isunshaded. As can be understood, an object 618 can occupy the occludedregion 616, but because the vehicle 102 may not receive sensor dataassociated with the occluded region 616, the vehicle 102 would not knowabout the presence of the object 618 (e.g., the corresponding occupancystate may be indeterminate).

In some instances, the operation 602 can include determining that theocclusion fields in the un-occluded region 614 are visible andunoccupied, while the occlusion fields in the occluded region 616 arenot visible and the occupancy state is unknown (e.g., indeterminate). Insome instances, a default state for an occlusion field in the occludedregion 616 can be an occupied state (or an indeterminate state to betreated as an occupied state).

At operation 620, the process can include controlling a vehicle tochange position based at least in part on the first evaluation of theocclusion grid (e.g., a determination that there is not enoughinformation to safely traverse the intersection). In an example 622, thevehicle 102 can be controlled to move from the stop line 606 to thecheck line 610 to increase a size of the un-occluded region 614 of theocclusion grid 608 (e.g., by increasing the amount of data availablefrom sensors of the vehicle 102), and accordingly, to reduce a size ofthe occluded region 616 of the occlusion grid 608. As illustrated in theexample 622, a majority of the occlusion grid 608 is un-occluded to thevehicle 102, although the vehicle 102 can now identify the object 618 inthe occlusion grid 608. Accordingly, occlusion fields of the occlusiongrid 608 that correspond to the object 618 are identified as an occupiedregion 624 (and, for the purposes of illustration, are shaded in black).Similarly, the object 618 can generate a “shadow” with respect to thesensed region of the vehicle 102, illustrated by the occluded region 634(and as shaded gray in the example 622). Accordingly, the vehicle 102can be controlled to wait at the check line 610 without further enteringthe intersection 110. In some examples, the vehicle 102 can becontrolled to advance slowly (e.g. under a threshold speed) to the checkline 610 (e.g., a “creep mode”), although any acceleration and/orvelocity can be used.

At operation 626, the process can include controlling the vehicle totraverse the intersection based at least in part on a second evaluationof the occlusion grid. In an example 628, the object 618 has exited theocclusion grid 608, which is now visible and unoccupied. Accordingly,the vehicle 102 is illustrated as traversing the intersection 110 basedat least in part on a trajectory 630. In some examples, the vehicle 102can be controlled to accelerate through the intersection 110 using anormal or high acceleration (e.g., a “boost mode”), although anyacceleration can be used.

In some instances, the operations 602, 620, and/or 626 can be performedcontinuously by the vehicle 102 to monitor a state of the environmentover time. In some instances, the process 600 can include evaluating theocclusion grid at a frequency of 10 Hertz (Hz) (or any frequency) tocontinuously evaluate the environment.

FIG. 7 is a pictorial flow diagram of another example process 700 forevaluating an occlusion grid at an intersection and controlling avehicle to traverse the intersection, in accordance with embodiments ofthe disclosure. For example, some or all of the process 700 can beperformed by one or more components in FIG. 4, as described herein.

At operation 702, the process can include evaluating an occlusion gridat an intersection. In some examples, the operation 702 can includegenerating an occlusion grid in response to identifying an obstacle inan environment. In an example 704, a vehicle 102 is at the intersection110 attempting to follow a trajectory 706, such as executing anunprotected left turn (e.g., the vehicle 102 does not have a green arrowand must yield to oncoming traffic). In some instances, the vehicle 102can identify an object 708 (e.g., a vehicle) in the environment and canassociate an occlusion grid 710 with the object 708. As discussedherein, the vehicle can receive LIDAR data and/or image data to evaluatethe occlusion state and/or occupancy state of occlusion fields in theocclusion grid 710.

At operation 712, the process can include controlling the vehicle 102 towait at the intersection. As can be understood, the vehicle 102 can becontrolled to wait at the intersection 110 based at least in part on aportion of the occlusion grid 710 being occluded, the occlusion grid 710providing an insufficient amount of information for safely traversingthe intersection 110, and/or based at least in part on the object 708traversing the intersection 110.

At operation 714, the process can include evaluating the occlusion gridas clear (or as otherwise providing sufficient information to enable asafe traversal of the intersection 110 according to the trajectory 706).In an example 716, the object 708 is illustrated as having navigated outof the occlusion grid 710 across the intersection 110. In some examples,the operation 714 can include capturing LIDAR data and/or image data todetermine that the occlusion fields are visible and not occupied (orotherwise indeterminate), and that accordingly, the occlusion grid 710is clear (or provides sufficient information).

At operation 718, the process can include controlling the vehicle 102 totraverse the intersection based at least in part on the occlusion gridbeing clear. In some instances, the operation 718 can include waitingfor a predetermined amount of time (e.g., 1 second, 2 seconds, etc.) forthe occlusion grid 710 to be clear (or provide a sufficient amount ofinformation to determine a safe traversal of trajectory 706) prior toinstructing the vehicle 102 to traverse the environment. As illustratedin the example 716, the vehicle 102 is traversing the intersection 110via the trajectory 706. Further, the occlusion grid 710 is illustratedas being visible and clear from obstacles.

FIG. 8 is a pictorial flow diagram of an example process 800 fordetermining an occlusion region associated with an object and forgenerating predicted trajectories for dynamic object(s) in the occlusionregion, in accordance with embodiments of the disclosure. For example,some or all of the process 800 can be performed by one or morecomponents in FIG. 4, as described herein. For example, some or all ofthe process 800 can be performed by the vehicle computing device(s) 404.

At operation 802, the process can include determining an occlusionregion associated with an object. In an example 804, the vehicle 102 atthe intersection 110 captures LIDAR data and/or image data of theenvironment. In some examples, the operation 802 can include identifyingan object 806 in the environment and an occlusion region 808 associatedwith the object 806. In the example 804, the object 806 can represent acar parked near the intersection 110. In some instances, the operation802 can include generating an occlusion grid (not shown) associated withthe object 806. In some instances, the occlusion region 808 representsan occluded portion of an occlusion grid. In some instances, theoperation 802 can include accessing map data including an occlusiongrid, as discussed herein.

At operation 810, the process can include identifying a dynamic object(e.g., a pedestrian) proximate to the occlusion region 808. In someinstances, the operation 810 can include determining that a dynamicobject (e.g., a pedestrian) 812 is within a threshold distance of theocclusion region 808. In some instances, the operation 810 can includereceiving LIDAR data, image data, and/or other sensor data to measure,as a measured trace 814, a trajectory (e.g., positions, orientations,and velocities) of the dynamic object 812 in the environment.

At operation 816, the process can include generating one or morepredicted trajectories for the dynamic object. In the example 804, oneor more predicted trajectories 818 are illustrated as possible paths forthe pedestrian 812 to follow while in the occlusion region 808 (althoughthe predicted trajectories are not limited to the occlusion region 808).In some examples, the predicted trajectories 818 can be based at leastin part on the measured trace 814 and/or symbols or regions in theenvironment. For example, a predicted trajectory may include acontinuation of the speed and direction of the pedestrian 812 as thepedestrian entered the occlusion region 808. In another example, apredicted trajectory may be based on a destination of the environment,such as a sidewalk on the opposite side of the intersection. Predictedtrajectories can be generated using a variety of methods. Details ofgenerating predicted trajectories are discussed in U.S. application Ser.No. 15/833,715, which is herein incorporated by reference, in itsentirety.

In some examples, the operation 816 can include controlling the vehicle102 based at least in part on the one or more predicted trajectories818. In some instances, if a trajectory of the vehicle 102 is within athreshold distance of at least one predicted trajectory, the vehicle 102can be controlled to wait until additional information about the dynamicobject can be determined. In some instances, as a period of timeelapses, the vehicle 102 can increase or decrease a probabilityassociated with individual predicted trajectories indicating alikelihood that the path of the pedestrian 812 follows one or more ofthe predicted trajectories 818. For example, as time progresses and anobservation associated with the dynamic object 812 is or is not made, atrajectory can be rejected (e.g., pruned) from the possible list ofpredicted trajectories. In some instances, as the dynamic object 812 isobserved in the environment, one or more predicted trajectories can berejected if they do not describe or are not associated with the movementof the dynamic object 812 within the occlusion region 808.

FIG. 9 is a pictorial flow diagram of an example process 900 forevaluating a topographic occlusion grid and for controlling a vehicle totraverse a topographic obstacle, in accordance with embodiments of thedisclosure. For example, some or all of the process 900 can be performedby one or more components in FIG. 4, as described herein. For example,some or all of the process 900 can be performed by the vehicle computingdevice(s) 404.

At operation 902, the process can include evaluating an occlusion grid.In some examples, the occlusion grid can be a topographic occlusion gridgenerated in response to topographic (e.g., geographic) features in anenvironment. In an example 904, an occlusion grid 906 is shown inconnection with a crest of a hill. As can be understood, as the vehicle102 approaches the hill, the crest of the hill naturally creates regionsthat are not visible to the vehicle 102 due to the height difference andangle of the ground. In one example, the occlusion grid 906 isassociated with an un-occluded region 908 and an occluded region 910. Asillustrated, the vehicle 102 emits LIDAR rays 912 that traverse throughthe occlusion grid 906 to identify the un-occluded region 908 and theoccluded region 910.

As can be understood, as the vehicle 102 approaches the crest of a hill,the vehicle 102 may not be able to capture sensor data of the ground atthe top of the hill. In some embodiments, this reduces the visibility ofthe vehicle 102, and accordingly, the vehicle 102 can reduce a speed ofthe vehicle 102 to navigate the environment.

However, at operation 914, the process can include determining anoccupancy of at least one occlusion field. For example, the occlusiongrid 906 includes at least one occlusion field 916. As illustrated inthe example 904, the occlusion field 916 is located at the top of thehill such that the LIDAR rays 912 cannot capture sensor data of theground associated with the occlusion field 916. In some instances, theocclusion grid 906 can be accessed via map data that includestopographical information (e.g., height information) of a region toindicate particular regions that would or would not be occluded.Further, such map information can indicate how many data points (e.g.,LIDAR returns) above a surface can be expected based on a location ofthe vehicle 102, for example. However, because the occlusion field 916comprises or is associated with a three-dimensional volume of space, theLIDAR rays 912 can traverse through a portion of the occlusion field916. In some examples, based at least in part on ray casting the LIDARrays 912, as discussed herein, the operation 914 can include determiningthe occupancy of the occlusion field 916. In this example, the occlusionfield 916 is visible to the vehicle 102 (for the purpose of determiningan occlusion state) and unoccupied by an obstacle.

At operation 918, the process can include controlling a vehicle (e.g.,the vehicle 102) based at least in part on the occupancy. For example,by evaluating the occlusion grid 906 that represents a hill in theenvironment, the vehicle 102 can “see over” the crest of the hilldespite the sensors not directly capturing ground data beyond the hillof the crest. Thus, the vehicle 102 can plan a trajectory and/ordetermine a velocity based on the occupancy of the occlusion grid 906beyond the crest of the hill. As a non-limiting example, the vehicle 102may adjust one or more of a velocity, orientation, or position such thatinformation provided by the occlusion grid 906 is sufficient to safelytraverse the hill.

FIG. 10 depicts an example process for determining an occupancy of anocclusion field based on ray casting LIDAR data and/or based onprojecting an occlusion field into image data, and controlling a vehicleto traverse an environment, in accordance with embodiments of thedisclosure. For example, some or all of the process 1000 can beperformed by one or more components in FIG. 4, as described herein. Forexample, some or all of the process 1000 can be performed by the vehiclecomputing device(s) 404.

At operation 1002, the process can include accessing map datarepresenting an environment proximate to an autonomous vehicle, the mapdata comprising an occlusion grid comprising an occlusion field. In someinstances, the operation 1002 can include determining a location of thevehicle in an environment and accessing map data that is within athreshold distance of the vehicle (e.g., a receding horizon). Asdiscussed herein, in some instances, the occlusion grid can comprise aplurality of occlusion fields, with each occlusion field representing adiscrete portion of the environment (e.g., part of a driveable surface).In some instances, the occlusion field can represent an occlusion state(e.g., whether the occlusion field is visible to the vehicle) and anoccupancy state (e.g., whether the occlusion field is occupied by anobject in the environment, or otherwise indeterminate).

At operation 1004, the process can include capturing sensor data using asensor on the autonomous vehicle. In some instances, the operation 1004can include capturing LIDAR data and/or image data of the environment.

At operation 1006, the process can include determining an occupancy ofthe occlusion field. In some examples, the operation 1006 can alsoinclude determining a visibility of the occlusion field. In someexamples, the operation 1006 can include determining the occupancy ofthe occlusion field based at least in part on LIDAR data (e.g.,operation 1008) and/or image data (e.g., operation 1010).

At operation 1008, the process can include ray casting the LIDAR data todetermine an occupancy of the occlusion field. As discussed inconnection with FIG. 2, and throughout this disclosure, ray casting theLIDAR data can include determining an expected number of LIDAR returnsthrough the occlusion field (e.g., which may pass through voxels in acolumn of voxel space above the occlusion field) and determining anactual number of LIDAR returns through the occlusion field with respectto the total number of voxels passing above such an occlusion field asdiscussed in detail above. Of course, the operation 1008 can includedetermining that the LIDAR data represents an object in the occlusionfield, in which case the occlusion field would be occupied.

If a threshold number (or percentage) of returns indicate no return, theoccupancy state may be unoccupied. Otherwise, the occupancy state may beindeterminate.

At operation 1010, the process can include projecting the occlusionfield into segmented image data to determine an occupancy of theocclusion field. As discussed in connection with FIG. 3, and throughoutthis disclosure, image data captured by image sensors can be segmentedto determine locations of driveable regions, vehicles, pedestrians, andthe like. A location of the occlusion field can be projected into thesegmented image data to determine if the location of the occlusion fieldcorresponds to a driveable surface. If yes, the occlusion field is notoccupied, but if the occlusion field projects into a vehicle, forexample, the occlusion field can be determined to be occupied, orotherwise indeterminate. A determination of whether the occlusion fieldis occupied can be made in the operation 1012.

At operation 1012, if a threshold number of occlusion fields areoccupied (“yes”) or indeterminate, the process continues to operation1014. At operation 1014, the process can include determining, base atleast in part on the occupancy, that the occlusion grid is not clear.For example, if there is an insufficient number of unoccupied occlusionfields to determine that a vehicle may safely traverse the environment,the process may return. In some instances, the operation 1014 caninclude determining that a threshold number of occlusion fields areoccupied or indeterminate. In some instances, the operation 1014 caninclude controlling the vehicle to navigate to location to observe alarger portion of the occlusion gird. The process can return to theoperation 1004 to capture additional data to determine states of theocclusion fields and, accordingly, of the occlusion grid.

If the occlusion field is not occupied (“no” in operation 1012), theprocess continues to operation 1016. At operation 1016, the process caninclude determining, based at least in part on the occupancy, that theocclusion grid is clear, or otherwise provides sufficient information tosafely traverse the environment.

At operation 1018, the process can include controlling the autonomousvehicle to traverse the environment based at least in part on theocclusion grid being clear.

FIG. 11 depicts an example process 1100 for determining an occlusiongrid based on map data being indicative of the occlusion grid or basedon identifying an obstacle in the environment, and controlling a vehicleto traverse the environment, in accordance with embodiments of thedisclosure. For example, some or all of the process 1100 can beperformed by one or more components in FIG. 4, as described herein. Forexample, some or all of the process 1100 can be performed by the vehiclecomputing device(s) 404.

At operation 1102, the process can include controlling an autonomousvehicle to traverse to a first location in an environment. In somecases, the first location can represent a stop line at an intersectionin an environment.

At operation 1104, the process can include capturing sensor data of theenvironment using a sensor on the autonomous vehicle. In some examples,the operation 1104 can include capturing LIDAR data, image data, RADARdata, and the like in the environment.

At operation 1106, the process can include determining 1) whether mapdata is indicative of an occlusion grid or 2) whether an obstacle isidentified in the environment. If “no” (e.g., the map data does notindicate that there is an occlusion grid associated with the locationand there is not an object identified in the environment), the processcan return to operation 1102 whereby the autonomous vehicle can move toa new location (operation 1102) and/or so that the autonomous vehiclecan capture sensor data in the environment (operation 1104) inaccordance with nominal driving behaviors (e.g., trajectory generationbased on sensor data). If “yes” in operation 1106 (e.g., the map dataindicates that there is an occlusion grid associated with the locationor there is an object identified in the environment), the processcontinues to operation 1108.

At operation 1108, the process can include determining an occlusion gridcomprising a plurality of occlusion fields, an occlusion fieldindicating a visibility and an occupancy of a corresponding region inthe environment. In some examples, the occlusion grid can be accessedfrom map data, and in some examples, the occlusion grid can be generateddynamically based at least in part on identifying an object.

At operation 1110, the process can include determining, as an occludedregion and based at least in part on the sensor data, that at least aportion of the occlusion grid is occluded at a first time. As discussedherein, the occluded region may not be visible to one or more sensors ofthe autonomous vehicle at the first time.

At operation 1112, the process can include the operation can includecontrolling the autonomous vehicle to stay at the first location or totraverse to a second location based at least in part on the occludedregion. An example of staying at the first location is discussed inconnection with FIG. 7, and an example of controlling the vehicle totraverse to a second location is discussed in connection with FIG. 6.For example, the second location can be a check line, allowing theautonomous vehicle to navigate to the second location to view more ofthe occlusion grid.

At operation 1114, the process can include determining, based at leastin part on the sensor data, that the occluded region (or the portion ofthe environment corresponding to the occluded region) is visible at asecond time. With respect to FIG. 6, the autonomous vehicle may navigateto the second location so that obstacles in the environment do not blockthe sensors from capturing data of the entirety of the occlusion grid.With respect to FIG. 7, the autonomous vehicle may wait for the obstacleto move, thereby allowing the sensors to capture data representing theocclusion grid.

At operation 1116, the process can include controlling the autonomousvehicle based at least in part on the occluded region being visible atthe second time. In some examples, the operation 1116 can also includedetermining that the occlusion grid is not occupied, indicating that theautonomous vehicle can safely traverse through the environment.

EXAMPLE CLAUSES

A: A system comprising: one or more processors; and one or morecomputer-readable media storing instructions executable by the one ormore processors, wherein the instructions, when executed, cause thesystem to perform operations comprising: capturing LIDAR data using aLIDAR sensor on an autonomous vehicle; accessing map data representingan environment proximate to the autonomous vehicle, the map datacomprising an occlusion grid comprising multiple occlusion fields,wherein an occlusion field of the multiple occlusion fields isassociated with an occlusion state and an occupancy state of a portionof the environment; ray casting a portion of the LIDAR data to determinethe occupancy state and the occlusion state of the occlusion field;determining, based at least in part on the occlusion field, asufficiency of information associated with the occlusion grid todetermine a safe trajectory for the autonomous vehicle to traverse theenvironment; and controlling the autonomous vehicle to traverse theenvironment in accordance with the safe trajectory.

B: The system of paragraph A, the operations further comprising:capturing image data using an image sensor on the autonomous vehicle;performing semantic segmentation on at least a portion of the image datato generate segmented image data identifying at least a driveablesurface in the environment; projecting the occlusion field into thesegmented image data; determining that the occlusion field projects intothe driveable surface; and controlling the autonomous vehicle totraverse the environment based at least in part on the occlusion fieldprojecting into the driveable surface.

C: The system of paragraph A or B, wherein the multiple occlusion fieldsdiscretize a portion of a driveable surface in the environment, whereinthe occlusion state of the occlusion field is indicative of a visibilityof the occlusion field by the LIDAR sensor.

D: The system of any of paragraphs A-C, the operations furthercomprising: determining a confidence value associated with the occupancystate of the occlusion field based at least in part on: a first numberof LIDAR returns associated with the occlusion field; and a secondnumber of discretized regions associated with the occlusion field.

E: The system of paragraph D, wherein a first ratio of the first numberwith respect to the second number being below a threshold is indicativeof a high confidence of an unoccupied state of the occlusion field, anda second ratio of the first number with respect to the second numberbeing above the threshold is indicative of a high confidence of anoccupied state of the occlusion field.

F: A method comprising: capturing sensor data using a sensor on arobotic platform; determining, based at least in part on the sensordata, a location of the robotic platform; accessing map datarepresenting an environment proximate to the robotic platform at thelocation, the map data comprising an occlusion grid; determining anoccupancy state of an occlusion field of the occlusion grid;determining, based at least in part on the occlusion field, asufficiency of information to determine a trajectory to traverse an areaof the environment proximate to the occlusion grid; and controlling therobotic platform based at least in part on the occupancy state of theocclusion grid.

G: The method of paragraph F, further comprising: capturing, as thesensor data, LIDAR data using a LIDAR sensor on the robotic platform;ray casting a portion of the LIDAR data to determine the occupancy stateof the occlusion field, the occupancy state comprising one of anoccupied state, an unoccupied state, or an indeterminate state; raycasting a portion of the LIDAR data to determine an occlusion state ofthe occlusion field, the occlusion state comprising one of visible oroccluded; and controlling the robotic platform to traverse theenvironment based at least in part on the occlusion grid being clear,wherein the sufficiency of information is determined based at least inpart on a number of occlusion fields of the occlusion grid that arevisible to the LIDAR sensor relative to at least one of a size of theocclusion grid or a speed limit associated with the occlusion grid.

H: The method of paragraph G, wherein the ray casting comprises:determining a first number of expected LIDAR returns associated with aregion above the occlusion field; determining a second number of actualLIDAR returns associated with the region above the occlusion field;determining a confidence value associated with the occupancy of theocclusion field based at least in part on the first number of expectedLIDAR returns and the second number of actual LIDAR returns; anddetermining that the confidence value meets or exceeds a thresholdconfidence value.

I: The method of any of paragraphs F-H, further comprising: capturing,as the sensor data, image data using an image sensor on the roboticplatform; performing segmentation on at least a portion of the imagedata to generate segmented image data identifying a driveable surface inthe environment; projecting the occlusion field into the segmented imagedata; determining that the occlusion field projects into the driveablesurface; and controlling the robotic platform to traverse theenvironment based at least in part on the occlusion field projectinginto the driveable surface.

J: The method of any of paragraphs F-I, wherein the robotic platformcomprises an autonomous vehicle and the environment proximate to theautonomous vehicle comprises an intersection, the method furthercomprising: determining a distance of the intersection for theautonomous vehicle to traverse; determining a speed limit associatedwith the intersection; and determining an extent of the occlusion gridbased at least in part on: the distance of the intersection; the speedlimit; and a safety factor.

K: The method of paragraph J, further comprising determining the extentof the occlusion grid based at least in part on an expected amount oftime for the autonomous vehicle to traverse the distance of theintersection.

L: The method of any of paragraphs F-K, wherein the occlusion fieldcomprises a temporal logic symbol, the method further comprising:validating a trajectory based at least in part on evaluating a temporallogic formula comprising the temporal logic symbol; and controlling therobotic platform to traverse the environment based at least in part onthe trajectory.

M: The method of any of paragraphs F-L, wherein the occlusion gridcomprises a plurality of occlusion fields, the method furthercomprising: determining that a threshold number of the plurality ofocclusion fields are visible to the sensor and are unoccupied prior tocontrolling the robotic platform to traverse the environment.

N: The method of any of paragraphs F-M, further comprising: determiningthat a portion of the occlusion grid is not visible to the sensor; andcontrolling the robotic platform to move to a location to increase theportion of the occlusion grid that is visible to the sensor.

O: A non-transitory computer-readable medium storing instructions that,when executed, cause one or more processors to perform operationscomprising: receiving sensor data captured using a sensor on a roboticplatform; determining, based at least in part on the sensor data, alocation of the robotic platform accessing map data representing anenvironment proximate to the robotic platform at the location, the mapdata comprising an occlusion grid; determining an occupancy state of anocclusion field of the occlusion grid; determining, based at least inpart on the occlusion field, a sufficiency of information to determine atrajectory to traverse an area of the environment proximate to theocclusion grid; and controlling the robotic platform based at least inpart on the state of the occlusion grid.

P: The non-transitory computer-readable medium of paragraph O, whereinthe sensor data is LIDAR data and wherein the sensor is a LIDAR sensor,the operations further comprising: ray casting a portion of the LIDARdata to determine the occupancy state and an occlusion state of theocclusion field; determining, based at least in part on the occupancystate and the occlusion state, the sufficiency of information associatedwith the occlusion grid; and controlling the robotic platform totraverse the environment based at least in part on the sufficiency ofinformation.

Q: The non-transitory computer-readable medium of paragraph P, whereinthe ray casting comprises: determining a first number of expected LIDARreturns associated with a region above the occlusion field; determininga second number of actual LIDAR returns associated with the region abovethe occlusion field; determining a confidence value associated with theoccupancy state of the occlusion field based at least in part on thefirst number of expected LIDAR returns and the second number of actualLIDAR returns; and determining that the confidence value meets orexceeds a threshold confidence value, wherein the confidence levelmeeting or exceeding the threshold confidence value indicates that theocclusion field is unoccupied.

R: The non-transitory computer-readable medium of any of paragraphs O-Q,capturing, as the sensor data, image data using an image sensor on therobotic platform; performing segmentation on at least a portion of theimage data to generate segmented image data identifying a driveablesurface in the environment; projecting the occlusion field into thesegmented image data; and determining that the occlusion field projectsinto the driveable surface.

S: The non-transitory computer-readable medium of any of paragraphs O-R,the operations further comprising: determining that a portion of theocclusion grid is not visible to the sensor; and controlling the roboticplatform to move to a location where an entirety of the occlusion gridis visible to the sensor, wherein a velocity of the robotic platform isbelow a velocity threshold.

T: The non-transitory computer-readable medium of any of paragraphs O-S,wherein the sensor data is first sensor data, the operations furthercomprising: receiving, as the first sensor data, image data captured byan image sensor on the robotic platform; receiving, as second sensordata, LIDAR data captured by a LIDAR sensor on the robotic platform; anddetermining, based at least in part on the first sensor data and thesecond sensor data, a confidence value associated with the occupancystate of the occlusion field.

U: A system comprising: one or more processors; and one or morecomputer-readable media storing instructions executable by the one ormore processors, wherein the instructions, when executed, cause thesystem to perform operations comprising: controlling an autonomousvehicle to traverse to a first location in an environment; capturing, assensor data, at least LIDAR data or image data of the environment usinga sensor of the autonomous vehicle; determining, based at least in parton accessing map data or on identifying an obstacle in an environment,an occlusion grid comprising a plurality of occlusion fields, anocclusion field of the occlusion grid indicating an occlusion state andan occupancy state of a corresponding region of the environment;determining, as an occluded region and based at least in part on thesensor data, that at least a first portion of the occlusion grid isoccluded at a first time; determining, as an un-occluded region andbased at least in part on the sensor data, that at least a secondportion of the occlusion grid is un-occluded at the first time;determining, based at least in part on an extent and occupancy of thesecond portion, a confidence level associated with safely traversing atrajectory through the environment; controlling the autonomous vehicleto stay at the first location or to traverse to a second location basedat least in part on the confidence level being below a threshold level;determining, based at least in part on the sensor data, that a thirdportion of the occlusion grid is visible and unoccupied at a secondtime; and controlling the autonomous vehicle based at least in part onthe third portion of the occlusion grid being visible an unoccupied atthe second time.

V: The system of paragraph U, the operations further comprising one ormore of: determining that the autonomous vehicle is approaching anintersection where the autonomous vehicle is required to yield to othertraffic; determining that the obstacle is associated with a dynamicocclusion grid; determining a predicted trajectory of a dynamic obstaclethat traversed within the occlusion grid; or determining the occlusiongrid based on a topographic occlusion region.

W: The system of paragraph U or V, wherein the sensor data comprises theLIDAR data and the sensor is a LIDAR sensor, the operations furthercomprising: ray casting a portion of the LIDAR data to determine theocclusion state and the occupancy state of the occlusion field.

X: The system of any of paragraphs U-W, wherein the sensor data is imagedata and the sensor is an image sensor, the operations furthercomprising: performing segmentation on at least a portion of the imagedata to generate segmented image data identifying a driveable surface inthe environment; projecting the occlusion field into the segmented imagedata; determining that the occlusion field projects into the driveablesurface; and controlling the autonomous vehicle to traverse theenvironment based at least in part on the occlusion field projectinginto the driveable surface.

Y: The system of any of paragraphs U-X, the operations furthercomprising: determining a first number of un-occluded and unoccupiedocclusion fields of the plurality of occlusion fields; determining thatthe first number does not meet or exceed a threshold number of occlusionfields associated with safe traversal of the environment; andcontrolling the autonomous vehicle to stay at the first location or totraverse to the second location based at least in part on the firstnumber not meeting or exceeding the threshold number.

Z: A method comprising: controlling an autonomous vehicle to traverse toa first location in an environment; determining, based at least in parton accessing map data or on identifying an obstacle in an environment,an occlusion grid; determining, as an occluded region, that at least afirst portion of the occlusion grid is occluded at a first time;determining, as an un-occluded region, that at least a second portion ofthe occlusion grid is visible at the first time; determining anoccupancy state of the un-occluded region; controlling the autonomousvehicle to stay at the first location or traverse to a second locationbased at least in part on an extent of the un-occluded region and theoccupancy state of the un-occluded region; determining that a thirdportion of the occlusion grid is un-occluded and unoccupied at a secondtime; and controlling the autonomous vehicle based at least in part onthe third portion of the occlusion grid being un-occluded and unoccupiedat the second time.

AA: The method of paragraph Z, further comprising: determining that theautonomous vehicle is approaching an intersection in an environment,wherein the autonomous vehicle lacks a right of way at the intersection;wherein: the first location is a stop line associated with theintersection; and the second location is a check line at least partiallypast the stop line and within the intersection.

AB: The method of paragraph AA, further comprising: controlling theautonomous vehicle to traverse from the first location to the secondlocation at a first velocity below a threshold velocity; determiningcapturing sensor data at the second location; determining, based atleast in part on the sensor data, that the third portion is un-occludedand unoccupied at the second time; determining that an extent of thethird portion meets or exceeds a threshold; and controlling theautonomous vehicle to traverse from the second location through theintersection at a second velocity above the threshold velocity.

AC: The method of any of paragraphs Z-AB, wherein the obstacle is adynamic obstacle, the method further comprising; identifying the dynamicobstacle in the environment; associating the occlusion grid with thedynamic obstacle; controlling the autonomous vehicle to stay at thefirst location based at least in part on the extent of the un-occludedregion and the occupancy state of the un-occluded region at the firsttime; and controlling the autonomous vehicle based at least in part onthe third portion of the occlusion grid being un-occluded and unoccupiedat the second time.

AD: The method of any of paragraphs Z-AC, further comprising:identifying the obstacle in the environment; associating the occlusiongrid with the obstacle; identifying a dynamic object in the environment;capturing motion data of the dynamic object in the environment;determining that the dynamic object traversed into the occluded region;determining a predicted trajectory for the dynamic object within theoccluded region; and controlling the autonomous vehicle based at leastin part on the predicted trajectory of the dynamic object.

AE: The method of any of paragraphs Z-AD, further comprising:determining that the autonomous vehicle is within a threshold distanceto a topographic occlusion region; and determining, as a topographicocclusion grid, the occlusion grid based at least in part on the mapdata associated with the topographic occlusion region.

AF: The method of any of paragraphs Z-AE, further comprising: capturingLIDAR data using a LIDAR sensor on the autonomous vehicle; and raycasting a portion of the LIDAR data to determine an occlusion state andan occupancy state of an occlusion field, the occlusion fieldrepresenting a portion of the occlusion grid.

AG: The method of any of paragraphs Z-AF, further comprising: capturingimage data using an image sensor on the autonomous vehicle; performingsegmentation on at least a portion of the image data to generatesegmented image data identifying a driveable surface in the environment;projecting an occlusion field into the segmented image data, theocclusion field representing a portion of the occlusion grid;determining that the occlusion field projects into the driveablesurface; and controlling the autonomous vehicle to traverse theenvironment based at least in part on the occlusion field projectinginto the driveable surface.

AH: The method of any of paragraphs Z-AG, further comprising:controlling the autonomous vehicle to traverse a portion of theenvironment based on a velocity selected based at least in part on afirst extent of the portion to traverse in the environment and a secondextent of the third portion of the occlusion grid being un-occluded andunoccupied.

AI: The method of any of paragraphs Z-AH, further comprising:determining a time period associated with the occluded region;determining that no objects have entered the occluded region during thetime period; and determining, based at least in part on a speed limitassociated with the occluded region, a confidence level associated withan occupancy of the occluded region based at least in part on the timeperiod.

AJ: A non-transitory computer-readable medium storing instructions that,when executed, cause one or more processors to perform operationscomprising: controlling a robotic platform to traverse to a firstlocation in an environment; determining a location of the roboticplatform with respect to a map, the map comprising map data;determining, based at least in part on accessing the map data associatedwith the location or on identifying an obstacle in an environmentproximate to the robotic platform, an occlusion grid; determining, as anoccluded region, that at least a first portion of the occlusion grid isoccluded at a first time; determining, as an un-occluded region, that atleast a second portion of the occlusion grid is visible at the firsttime; determining an occupancy state of the un-occluded region;controlling the robotic platform to stay at the first location ortraverse to a second location based at least in part on an extent of theun-occluded region and the occupancy state of the un-occluded region;determining that a third portion of the occlusion grid is un-occludedand unoccupied at a second time; and controlling the robotic platformbased at least in part on the third portion being un-occluded andunoccupied at the second time.

AK: The non-transitory computer-readable medium of paragraph AJ, theoperations further comprising: determining that the robotic platform isapproaching an intersection in an environment, wherein the roboticplatform is required to yield to other vehicles; wherein: the firstlocation is a stop line associated with the intersection; and the secondlocation is a check line at least partially past the stop line andwithin the intersection.

AL: The non-transitory computer-readable medium of paragraph AJ or AK,the operations further comprising: capturing LIDAR data using a LIDARsensor on the robotic platform; ray casting a portion of the LIDAR datato determine an occupancy of an occlusion field, the occlusion fieldrepresenting a portion of the occlusion grid; and determining, based atleast in part on the occupancy, that the occlusion grid is visible andunoccupied.

AM: The non-transitory computer-readable medium of any of paragraphsAJ-AL, the operations further comprising: capturing image data using animage sensor on the robotic platform; performing segmentation on atleast a portion of the image data to generate segmented image dataidentifying a driveable surface in the environment; projecting anocclusion field into the segmented image data, the occlusion fieldrepresenting a portion of the occlusion grid; determining that theocclusion field projects into the driveable surface; and controlling therobotic platform to traverse the environment based at least in part onthe occlusion field projecting into the driveable surface.

AN: The non-transitory computer-readable medium of any of paragraphsAJ-AM, the operations further comprising: determining a time periodassociated with the occluded region; determining that no objects haveentered the occluded region during the time period; and determining aconfidence level associated with a safe trajectory.

While the example clauses described above are described with respect toone particular implementation, it should be understood that, in thecontext of this document, the content of the example clauses can also beimplemented via a method, device, system, a computer-readable medium,and/or another implementation.

Conclusion

While one or more examples of the techniques described herein have beendescribed, various alterations, additions, permutations and equivalentsthereof are included within the scope of the techniques describedherein.

In the description of examples, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific examples of the claimed subject matter. It is to be understoodthat other examples can be used and that changes or alterations, such asstructural changes, can be made. Such examples, changes or alterationsare not necessarily departures from the scope with respect to theintended claimed subject matter. While the steps herein can be presentedin a certain order, in some cases the ordering can be changed so thatcertain inputs are provided at different times or in a different orderwithout changing the function of the systems and methods described. Thedisclosed procedures could also be executed in different orders.Additionally, various computations that are herein need not be performedin the order disclosed, and other examples using alternative orderingsof the computations could be readily implemented. In addition to beingreordered, the computations could also be decomposed intosub-computations with the same results.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer-readable media storing instructionsexecutable by the one or more processors, wherein the instructions, whenexecuted, cause the system to perform operations comprising: capturingLIDAR data using a LIDAR sensor on an autonomous vehicle; accessing mapdata representing an environment proximate to the autonomous vehicle,the map data comprising an occlusion grid comprising multiple occlusionfields, wherein an occlusion field of the multiple occlusion fields isassociated with an occlusion state and an occupancy state of a portionof the environment; ray casting a portion of the LIDAR data to determinethe occupancy state and the occlusion state of the occlusion field;determining, based at least in part on the occlusion field, an amount ofinformation associated with the occlusion grid to determine a safetrajectory for the autonomous vehicle to traverse the environment,wherein the amount of information comprises one or more of: a firstnumber of occlusion fields associated with an unoccupied occupancystate; a second number of observations associated with the occlusionfield and a period of time; a third number of occlusion fieldsassociated with a visible occlusion state; or a size of an unoccupiedand un-occluded portion of the environment; determining that the amountof information meets or exceeds a threshold level; and controlling theautonomous vehicle to traverse the environment in accordance with thesafe trajectory.
 2. The system of claim 1, the operations furthercomprising: capturing image data using an image sensor on the autonomousvehicle; performing semantic segmentation on at least a portion of theimage data to generate segmented image data identifying at least adriveable surface in the environment; projecting the occlusion fieldinto the segmented image data; determining that the occlusion fieldprojects into the driveable surface; and controlling the autonomousvehicle to traverse the environment based at least in part on theocclusion field projecting into the driveable surface.
 3. The system ofclaim 1, wherein the multiple occlusion fields discretize a portion of adriveable surface in the environment, wherein the occlusion state of theocclusion field is indicative of a visibility of the occlusion field bythe LIDAR sensor.
 4. The system of claim 1, the operations furthercomprising: determining a confidence value associated with the occupancystate of the occlusion field based at least in part on: a fourth numberof LIDAR returns associated with the occlusion field; and a fifth numberof discretized regions associated with the occlusion field.
 5. Thesystem of claim 4, wherein a first ratio of the fourth number withrespect to the fifth number being below a threshold is indicative of ahigh confidence of an unoccupied state of the occlusion field, and asecond ratio of the fourth number with respect to the fifth number beingabove the threshold is indicative of a high confidence of an occupiedstate of the occlusion field.
 6. A method comprising: capturing sensordata using a sensor on a robotic platform; determining, based at leastin part on the sensor data, a location of the robotic platform;accessing map data representing an environment proximate to the roboticplatform at the location, the map data comprising an occlusion grid;determining an occupancy state of an occlusion field of the occlusiongrid, wherein the occupancy state comprises one of an occupied state, anunoccupied state, or an indeterminate state; determining, based at leastin part on the occlusion field, an amount of information associated withthe occlusion grid to determine a trajectory to traverse an area of theenvironment proximate to the occlusion grid, wherein the amount ofinformation comprises one or more of: a first number of occlusion fieldsassociated with the unoccupied state; a second number of observationsassociated with the occlusion field and a period of time; or a size ofan unoccupied portion of the environment; determining that the amount ofinformation meets or exceeds a threshold value; and controlling therobotic platform based at least in part on the occupancy state of theocclusion grid and the amount of information.
 7. The method of claim 6,further comprising: capturing, as the sensor data, LIDAR data using aLIDAR sensor on the robotic platform; ray casting a portion of the LIDARdata to determine the occupancy state of the occlusion field; raycasting a portion of the LIDAR data to determine an occlusion state ofthe occlusion field, the occlusion state comprising one of visible oroccluded; and controlling the robotic platform to traverse theenvironment based at least in part on the occlusion grid being clear,wherein the amount of information further comprises a third number ofocclusion fields of the occlusion grid that are visible to the LIDARsensor.
 8. The method of claim 7, wherein the ray casting comprises:determining a first number of expected LIDAR returns associated with aregion above the occlusion field; determining a second number of actualLIDAR returns associated with the region above the occlusion field;determining a confidence value associated with the occupancy state ofthe occlusion field based at least in part on the first number ofexpected LIDAR returns and the second number of actual LIDAR returns;and determining that the confidence value meets or exceeds a thresholdconfidence value.
 9. The method of claim 6, further comprising:capturing, as the sensor data, image data using an image sensor on therobotic platform; performing segmentation on at least a portion of theimage data to generate segmented image data identifying a driveablesurface in the environment; projecting the occlusion field into thesegmented image data; determining that the occlusion field projects intothe driveable surface; and controlling the robotic platform to traversethe environment based at least in part on the occlusion field projectinginto the driveable surface.
 10. The method of claim 6, wherein therobotic platform comprises an autonomous vehicle and the environmentproximate to the autonomous vehicle comprises an intersection, themethod further comprising: determining a distance of the intersectionfor the autonomous vehicle to traverse; determining a speed limitassociated with the intersection; and determining an extent of theocclusion grid based at least in part on: the distance of theintersection; the speed limit; and a safety factor.
 11. The method ofclaim 10, further comprising determining the extent of the occlusiongrid based at least in part on an expected amount of time for theautonomous vehicle to traverse the distance of the intersection.
 12. Themethod of claim 6, wherein the occlusion field comprises a temporallogic symbol, the method further comprising: validating a trajectorybased at least in part on evaluating a temporal logic formula comprisingthe temporal logic symbol; and controlling the robotic platform totraverse the environment based at least in part on the trajectory. 13.The method of claim 6, wherein the occlusion grid comprises a pluralityof occlusion fields, the method further comprising: determining that athreshold number of the plurality of occlusion fields are visible to thesensor and are unoccupied prior to controlling the robotic platform totraverse the environment.
 14. The method of claim 6, further comprising:determining that a portion of the occlusion grid is not visible to thesensor; and controlling the robotic platform to move to a location toincrease the portion of the occlusion grid that is visible to thesensor.
 15. One or more non-transitory computer-readable media storinginstructions that, when executed, cause one or more processors toperform operations comprising: receiving sensor data captured using asensor on a robotic platform; determining, based at least in part on thesensor data, a location of the robotic platform; accessing map datarepresenting an environment proximate to the robotic platform at thelocation, the map data comprising an occlusion grid; determining anoccupancy state of an occlusion field of the occlusion grid;determining, based at least in part on the occlusion field, an amount ofinformation associated with the occlusion grid to determine a trajectoryto traverse an area of the environment proximate to the occlusion grid,wherein the amount of information comprises one or more of: a firstnumber of occlusion fields associated with an unoccupied occupancystate; a second number of observations associated with the occlusionfield and a period of time; or a size of an unoccupied portion of theenvironment; determining that the amount of information meets or exceedsa threshold value; and controlling the robotic platform based at leastin part on the occupancy state of the occlusion grid and the amount ofinformation.
 16. The one or more non-transitory computer-readable mediaof claim 15, wherein the sensor data is LIDAR data and wherein thesensor is a LIDAR sensor, the operations further comprising: ray castinga portion of the LIDAR data to determine the occupancy state and anocclusion state of the occlusion field; determining, based at least inpart on the occupancy state and the occlusion state, the amount ofinformation associated with the occlusion grid, wherein the amount ofinformation further comprises a third number of occlusion fieldsassociated with a visible occlusion state; and controlling the roboticplatform to traverse the environment further based at least in part onthe amount of information meeting or exceeding the threshold value. 17.The one or more non-transitory computer-readable media of claim 16,wherein the ray casting comprises: determining a first number ofexpected LIDAR returns associated with a region above the occlusionfield; determining a second number of actual LIDAR returns associatedwith the region above the occlusion field; determining a confidencevalue associated with the occupancy state of the occlusion field basedat least in part on the first number of expected LIDAR returns and thesecond number of actual LIDAR returns; and determining that theconfidence value meets or exceeds a threshold confidence value, whereinthe confidence value meeting or exceeding the threshold confidence valueindicates that the occlusion field is unoccupied.
 18. The one or morenon-transitory computer-readable media of claim 15, capturing, as thesensor data, image data using an image sensor on the robotic platform;performing segmentation on at least a portion of the image data togenerate segmented image data identifying a driveable surface in theenvironment; projecting the occlusion field into the segmented imagedata; and determining that the occlusion field projects into thedriveable surface.
 19. The one or more non-transitory computer-readablemedia of claim 15, the operations further comprising: determining that aportion of the occlusion grid is not visible to the sensor; andcontrolling the robotic platform to move to a location where an entiretyof the occlusion grid is visible to the sensor, wherein a velocity ofthe robotic platform is below a velocity threshold.
 20. The one or morenon-transitory computer-readable media of claim 15, wherein the sensordata is first sensor data, the operations further comprising: receiving,as the first sensor data, image data captured by an image sensor on therobotic platform; receiving, as second sensor data, LIDAR data capturedby a LIDAR sensor on the robotic platform; and determining, based atleast in part on the first sensor data and the second sensor data, aconfidence value associated with the occupancy state of the occlusionfield.